MIT Press, 2016. — 802 p. A comprehensive introduction to neural networks and deep learning by leading researchers of this field. Written for two main target audiences: university students (undergraduate or graduate) learning about machine learning, and software engineers. This is a PDF compilation of online book (www.deeplearningbook.org) Who Should Read This Book? Historical...
Manning, 2018. — 386 p. — ISBN: 9781617294433. This book introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications...
Apress, 2017. — 169 p. — ISBN: 978-1-4842-2765-7. Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is...
LazyProgrammer, 2016. — 71 p. Deep learning is making waves. At the time of this writing (March 2016), Google’s AlghaGo program just beat 9-dan professional Go player Lee Sedol at the game of Go, a Chinese board game. Experts in the field of Artificial Intelligence thought we were 10 years away from achieving a victory against a top professional Go player, but progress seems to...
Shelter Island: Manning, 2019. — 311 p. About this Book Welcome to Why you should learn deep learning Why you should read this book What you need to get started Fundamental Concepts What is deep learning? What is machine learning? Supervised machine learning Unsupervised machine learning Parametric vs nonparametric learning Supervised parametric learning Unsupervised parametric...
Manning, 2018. — 325 p. Artificial intelligence has made some incredible leaps. Deep learning systems now deliver near-human speech and image recognition, not to mention machines capable of beating world champion Go masters. Deep learning applies to a widening range of problems, such as question answering, machine translation, and optical character recognition. It's behind...
Packt, 2018. — 442 p. — ISBN 978-1-78839-990-6 Deep learning is a popular subset of machine learning, and it allows you to build complex models that are faster and give more accurate predictions. This book is your companion to take your first steps into the world of deep learning, with hands-on examples to boost your understanding of the topic. This book starts with a quick...
Packt Publishing, 2017. — 330 p. — ISBN: 178712519X. Solve different problems in modeling deep neural networks using Python, Tensorflow, and Keras with this practical guide. Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a...
New York: CreateSpace Independent Publishing Platform, 2016. — 212 p. Master Deep Time Series Forecasting with Python! Deep Time Series Forecasting with Python takes you on a gentle, fun and unhurried practical journey to creating deep neural network models for time series forecasting with Python. It uses plain language rather than mathematics; And is designed for working...
Springer, 2018. — 512 p. — ISBN: 3319944622. This book covers both classical and modern models in deep learning. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between...
With contributions by Nicholas Locascio. — New York: O’Reilly Media, 2017. — 298 p. — ISBN: 978-1-491-92561-4. With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research that is paving the way for modern machine learning. This book uses exposition and examples to help you understand major concepts in this complicated...
CreateSpace Independent Publishing Platform, 2016. — 251 p. — ISBN: 1519514212, 9781519514219 Master Deep Learning with this fun, practical, hands on guide. With the explosion of big data deep learning is now on the radar. Large companies such as Google, Microsoft, and Facebook have taken notice, and are actively growing in-house deep learning teams. Other large corporations...
Manning Publications, 2017. — 384 p. Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher Fran?ois Chollet, this book builds your understanding through intuitive explanations and practical examples. About the Technology Machine learning has made remarkable...
O’Reilly Media, 2017. — 538 p. — ISBN: 978-1-491-91425-0. True PDF Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning — especially deep neural networks — make a real difference in your organization? This hands-on guide not only provides the most practical information...
Amazon Digital Services, 2018. — 1750 p. People are using the tools of deep learning to change how we think about science, art, engineering, business, medicine, and even music. This book is for people who want to understand this field well enough to create deep learning systems, train them, and then use them with confidence to make their own contributions. The book takes a...
Apress, 2018. — 290 p. — ISBN: 148423684X. Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models. You’ll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP...
Amazon Digital Services, 2018. — 1750 p. People are using the tools of deep learning to change how we think about science, art, engineering, business, medicine, and even music. This book is for people who want to understand this field well enough to create deep learning systems, train them, and then use them with confidence to make their own contributions. The book takes a...
Packt Publishing, 2018. — 272 p. — ISBN: 1788837991. Dive deeper into neural networks and get your models trained, optimized with this quick reference guide. Deep learning has become an essential necessity to enter the world of artificial intelligence. With this book deep learning techniques will become more accessible, practical, and relevant to practicing data scientists. It...
Amazon Digital Services LLC, 2018. — 108 p. Deep learning is a process that widens the range of most artificial intelligence problems like speech recognition, image classification, question answering, optical character recognition, and transforming text to speech. It is true that deep learning is a complex subject to learn and understand, but it is not difficult for most...
PyImageSearch, 2017. — 321 p. The ImageNet Bundle is the most in-depth bundle and is for readers who want to train large-scale deep neural networks. Welcome to the Practitioner Bundle of Deep Learning for Computer Vision with Python! This volume is meant to be the next logical step in your deep learning for computer vision education after completing the Starter Bundle. At this...
LazyProgrammer, 2016. — 59 p. This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural...
NOWPress, 2013. — 198 p. This monograph provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks. The application areas are chosen with the following three criteria in mind: (1) expertise or knowledge of the authors; (2) the application areas that have already been transformed by the successful use of...
Addison-Wesley Professional, 2019. — 871 p. — (Addison-Wesley Data & Analytics Series). — ISBN: 0135116694. Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely visual, intuitive, and accessible , and yet offers a comprehensive introduction...
O’Reilly Media, 2019. — 55 p. — ISBN: 1492041947. Generative modeling is one of the hottest topics in artificial intelligence (AI). Recent advances in the field have shown how it’s possible to teach a machine to excel at human endeavors — such as drawing, composing music, and completing tasks — by generating an understanding of how its actions affect its environment. With this...
Packt Publishing, 2018. — 334 p. A hands-on guide to deep learning thats filled with intuitive explanations and engaging practical examples Key Features Designed to iteratively develop the skills of Python users who dont have a data science background Covers the key foundational concepts youll need to know when building deep learning systems Full of step-by-step exercises and...
Amazon Digital Services LLC, 2017. — 75 p. — ISBN: 1981614060. Neural Networks and Deep Learning: Deep Learning explained to your granny – A visual introduction for beginners who want to make their Deep Learning Neural Network (Machine Learning). Ready to crank up a neural network to get your self-driving car pick up the kids from school? Want to add 'Deep Learning’ to your...
Packt Publishing, 2017. — 300 p. — ISBN: 978-1-78646-582-5. True PDF Develop deep neural networks in Theano with practical code examples for image classification, machine translation, reinforcement agents, or generative models. This book offers a complete overview of Deep Learning with Theano, a Python-based library that makes optimizing numerical expressions and deep learning...
Apress, 2018. — 425 p. — ISBN: 1484237897. Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish),...
Packt Publishing, 2017. — 330 p. — ISBN: 178712519X. Solve different problems in modeling deep neural networks using Python, Tensorflow, and Keras with this practical guide. Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a...
Machine Learning Mastery Pty. Ltd. — 255 p. Deep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Tap into their power in a few lines of code using Keras, the best-of-breed applied deep learning library. In this mega Ebook is written in the friendly...
Amazon Kindle Publishing, 2019. — 170 p. — ISBN: 1092562222. Build your Own Neural Network through easy-to-follow instruction and examples. Thanks this easy tutorial you’ll learn the fundamentals of Deep learning and build your very own Neural Network in Python using TensorFlow, Keras, PyTorch, and Theano. While you have the option of spending thousands of dollars on a big and...
Apress, 2018. — 219 p. Discover the essential building blocks of the most common forms of deep belief networks. At each step this book provides intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with...
Packt Publishing, 2018. — 284 p. Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and...
2018. — 204 p. Artificial Intelligence is changing our lives, and solutions based on Deep Learning are leading this transformation. Deep Learning is now of major interest to private companies, since it can be applied to many areas of activity. But getting started in this technology is not an easy task. Many enthusiastic professionals in the field of Deep Learning have...
CreateSpace Independent Publishing Platform, 2018. — 245 p. — ISBN10: 1724716417, 13 978-1724716415. About the book: In Computer Sciences there is currently a gold rush mood due to a new field called "Deep Learning". But what is Deep Learning? This book is an introduction to Neural Networks and the most important Deep Learning model - the Convolutional Neural Network model...
John Wiley & Sons, Inc, 2019. — 442 p. — ISBN: 9781119543046. Take a deep dive into deep learning. Deep learning provides the means for discerning patterns in the data that drive online business and social media outlets. Deep Learning for Dummies gives you the information you need to take the mystery out of the topic — and all of the underlying technologies associated with it....
Manning Publications, 2019. — 384 p. Deep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. As you progress, you'll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You'll enjoy watching your bot master the game of Go, and along the way, you'll discover how to apply your new...
LazyProgrammer, 2016. — 71 p. Deep learning is making waves. At the time of this writing (March 2016), Google’s AlghaGo program just beat 9-dan professional Go player Lee Sedol at the game of Go, a Chinese board game. Experts in the field of Artificial Intelligence thought we were 10 years away from achieving a victory against a top professional Go player, but progress seems to...
New York: Apress, 2018. — 262 p. This book is a continuation of Volume I of this series. Extensive references are made to material in that volume. For this reason, it is strongly suggested that you be at least somewhat familiar with the material in Volume I. All techniques presented in this book are given modest mathematical justification, including the equations relevant to...
USA: Applied Data Science Partners Ltd, 2019. — 330 p. — ISBN: 978-1-492-04194-8. Generative modeling is one of the hottest topics in AI. It's now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most...
Apress, 2018. — 343 p. Discover the potential applications, challenges, and opportunities of deep learning from a business perspective with technical examples. These applications include image recognition, segmentation and annotation, video processing and annotation, voice recognition, intelligent personal assistants, automated translation, and autonomous vehicles. An...
Manning Publications, 2019. — 327 p. — ISBN: 1617294799. Deep Learning for Search is a practical book about how to use (deep) neural networks to help build effective search engines. This book examines several components of a search engine, providing insights on how they work and guidance on how neural networks can be used in each context. Emphasis is given to practical,...
The MIT Press, 2018. — 352 p. How deep learning — from Google Translate to driverless cars to personal cognitive assistants — is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading...
Springer, 2023. — 655 p. — ISBN: 3031454677. This book offers a comprehensive introduction to the central ideas that underpin deep learning. It is intended both for newcomers to machine learning and for those already experienced in the field. Covering key concepts relating to contemporary architectures and techniques, this essential book equips readers with a robust foundation...
Independently published, 2019. — 272 p. — ISBN: 978-1793223012. New 2019 Edition! Build Deeper is a complete and practical guide that can help you take the first few steps in deep learning. It will guide you step-by-step, from understanding the basic concepts, to building your first practical application. Deep Learning is the latest iteration of AI. Although the concept itself...
Apress, 2018. — 184 p. — ISBN: 148423720X. Discover the essential building blocks of a common and powerful form of deep belief network: convolutional nets. This book shows you how the structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a ‘thought process’ that is capable of learning abstract concepts built from...
Amazon Digital Services LLC, 2019. — 120 p. — ASIN B07GTT616W. Want to learn deep learning and AI, but hate math? This book is an experiment for me. After years of teaching successful deep learning and machine learning courses online, I’ve come to notice a few patterns. One of them is that a large subset of students just RUN AWAY at the sight of math. This is somewhat...
The MIT Press, 2019. — 187 p. — ISBN: 9780262039512. A project-based guide to the basics of deep learning. This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning....
Addison Wesley, 2018. — 320 p. — ISBN: 978-0135116692. Deep learning is one of today's hottest fields. This approach to machine learning is achieving breakthrough results in some of today's highest profile applications, in organizations ranging from Google to Tesla, Facebook to Apple. Thousands of technical professionals and students want to start leveraging its power, but...
Cham: Springer International Publishing, 2019. — 168 p. — ISBN: 978-3-030-06073-2. The purpose of this edited volume is to provide a comprehensive overview on the fundamentals of deep learning, introduce the widely-used learning architectures and algorithms, present its latest theoretical progress, discuss the most popular deep learning platforms and data sets, and describe how...
O’Reilly, 2018. — 400 p. — Early Release. With much success already attributed to deep learning, this discipline has started making waves throughout science broadly and the life sciences in particular. With this practical book, developers and scientists will learn how deep learning is used for genomics, chemistry, biophysics, microscopy, medical analysis, drug discovery, and...
Amazon Digital Services LLC, 2018. — 770 p. The last decade and some, has witnessed some remarkable advancements in the area of Deep Learning. This area of Artificial intelligence (AI) has proliferated into many branches - Deep Belief Networks, Recurrent Neural Networks, Convolution Neural Networks, Adversorial Networks, Reinforcement Learning, Capsule Networks and the list...
Third Edition, Packt Publishing, 2024. — 1114 p. ISBN: 978-1-80512-872-4. Unleash the full potential of transformers with this comprehensive guide covering architecture, capabilities, risks, and practical implementations on OpenAI, Google Vertex AI, and Hugging Face. Key Features Master NLP and vision transformers, from the architecture to fine-tuning and implementation. Learn...
Packt Publishing, 2017. — 330 p. — ISBN: 178712519X. Solve different problems in modeling deep neural networks using Python, Tensorflow, and Keras with this practical guide. Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a...
Unpublished, 2015. — 94 p. Deep learning is a framework for training and modeling neural networks which recently have surpassed all conventional methods in many learning tasks, prominently image and voice recognition. This thesis uses deep learning algorithms to forecast financial data. The deep learning framework is used to train a neural network. The deep neural network is a...
Edition 1.4. — 2018. — 574 p. Introduction Foundations Promise of Deep Learning for Time Series Forecasting Time Series Forecasting Convolutional Neural Networks for Time Series Recurrent Neural Networks for Time Series Promise of Deep Learning Extensions Further Reading Taxonomy of Time Series Forecasting Problems Framework Overview Inputs vs. Outputs Endogenous vs. Exogenous...
Packt, 2018. — 442 p. — ISBN 978-1-78839-990-6 Deep learning is a popular subset of machine learning, and it allows you to build complex models that are faster and give more accurate predictions. This book is your companion to take your first steps into the world of deep learning, with hands-on examples to boost your understanding of the topic. This book starts with a quick...
LazyProgrammer, 2016. — 59 p. This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural...
2018. — 132 p. — ISBN: 1727337964. Have you ever wanted to learn how to better use your data? Are you interested in the works of machine learning? If you answered yes to these questions, then this book is for you. Deep learning is a powerful data tool that can help improve businesses. In this book, you will learn: Neural networks Machine learning How it relates to certain...
New York: Springer, 2019. — 93 p. This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used to analyze big data in real-time, such as Hadoop, SPARK, and GRAPHX. Big data analytics using traditional techniques face various...
LazyProgrammer, 2016. — 46 p. When we talk about modern deep learning, we are often not talking about vanilla neural networks - but newer developments, like using Autoencoders and Restricted Boltzmann Machines to do unsupervised pre-training. Deep neural networks suffer from the vanishing gradient problem, and for many years researchers couldn’t get around it - that is, until...
Springer, 2019. — 188 p. — (Studies in Big Data 48). — ISBN: 978-3-030-01179-6. Deep Learning and Missing Data in Engineering Systems uses deep learning and swarm intelligence methods to cover missing data estimation in engineering systems. The missing data estimation processes proposed in the book can be applied in image recognition and reconstruction. To facilitate the...
GitHub, 2019. — 660 p. Just a few years ago, there were no legions of deep learning scientists developing intelligent products and services at major companies and startups. Machine learning was a forward-looking academic discipline with a narrow set of real-world applications. And those applications, e.g. speech recognition and computer vision, required so much domain knowledge...
O’Reilly Media, 2017. — 520 p. — ISBN: 978-1-491-91425-0. Looking for one central source where you can learn key findings on machine learning? Deep Learning: A Practitioner's Approach provides developers and data scientists with the most practical information available on the subject, including deep learning theory, best practices, and use cases. Authors Adam Gibson and Josh...
Springer, 2020. — 159 p. — (Studies in Big Data 57). — ISBN: 978-981-13-6793-9. This book introduces readers to both basic and advanced concepts in deep network models. It covers state-of-the-art deep architectures that many researchers are currently using to overcome the limitations of the traditional artificial neural networks. Various deep architecture models and their...
Packt Publishing, 2018. — 284 p. Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and...
Morgan & Claypool Publisher, 2017. - 124 p. - ISBN: 978-1627057288 Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of...
With contributions by Nicholas Locascio. — New York: O’Reilly Media, 2017. — 298 p. — ISBN: 978-1-491-92561-4. With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research that is paving the way for modern machine learning. This book uses exposition and examples to help you understand major concepts in this complicated...
2019. — 536 p. Artificial Intelligence (AI) especially Deep Learning (DL) has made tremendous progress in recent years. It start to spread in many areas, such as: image classification, voice recognition, text generation, language translation etc. As time goes by, it becomes apparent that deep learning will stay in the mainstream. As a technology people, it is time to keep...
Bleeding Edge Press, 2018. — 243 p. This book covers the crossroads of web development and deep learning. Both technologies are beginning to meet, and this honeymoon will produce new fantastic applications that you cannot even imagine yet. In this book you will see how to use the main JavaScript deep learning frameworks and web programming in the browser with the capture of...
Digital Services LLC, 2018. — 914 p. — ASIN: B079Y1M81K. People are using the tools of deep learning to change how we think about science, art, engineering, business, medicine, and even music. This book is for people who want to understand this field well enough to create deep learning systems, train them, and then use them with confidence to make their own contributions. The...
Digital Services LLC, 2018. — 910 p. — ASIN B079XSQNRX. People are using the tools of deep learning to change how we think about science, art, engineering, business, medicine, and even music. This book is for people who want to understand this field well enough to create deep learning systems, train them, and then use them with confidence to make their own contributions. The...
Apress, 2017. — 169 p. — ISBN: 978-1-4842-2765-7. Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is...
Manning Publishing, 2019. — 240 p. — ISBN: 978-1617295560. GANs in Action teaches you how to build and train your own Generative Adversarial Networks, one of the most important innovations in deep learning. In this book, you'll learn how to start building your own simple adversarial system as you explore the foundation of GAN architecture: the generator and discriminator...
Packt, 2018. — 271 p. — ISBN: 1785880365. Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train,...
Packt Publishing, 2018. — 436 p. — ISBN: 178899745X. True PDF Build and deploy powerful neural network models using the latest Java deep learning libraries Key Features Understand DL with Java by implementing real-world projects Master implementations of various ANN models and build your own DL systems Develop applications using NLP, image classification, RL, and GPU processing...
IGI Global, 2017. — 265 p. — ISBN: 978-1522530152. The expansion of digital data has transformed various sectors of business such as healthcare, industrial manufacturing, and transportation. A new way of solving business problems has emerged through the use of machine learning techniques in conjunction with big data analytics. Deep Learning Innovations and Their Convergence...
LazyProgrammer, 2016. — 46 p. When we talk about modern deep learning, we are often not talking about vanilla neural networks - but newer developments, like using Autoencoders and Restricted Boltzmann Machines to do unsupervised pre-training. Deep neural networks suffer from the vanishing gradient problem, and for many years researchers couldn’t get around it - that is, until...
Apress, 2019. — 294 p. — ISBN: 1484249755. Develop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In Advanced Applied Deep Learning, you will study advanced topics on CNN and object detection using Keras and TensorFlow. Along the...
Apress, 2018. — 290 p. — ISBN: 148423684X. Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models. You’ll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP...
New York: John Wiley & Sons, 2018. — 53 p. Are you looking for information to help you with your artificial intelligence deep learning journey? This Deep Learning Dummies guide will help you understand what AI, deep learning and machine learning can mean for you and your organization.
Academic Press, 2019. — 238 p. — (Computer Vision and Pattern Recognition). — ISBN: 978-0-12-813659-1. This book bridges classical sparse and low rank models — those that emphasize problem-specific Interpretability — with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is...
Manning Publications, 2018. — 392 p. — ISBN13: 978-1617295546. Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples. Machine learning has made remarkable progress in recent years. Deep-learning systems now...
Applied Data Science Partners Ltd, 2019. — 330 p. — ISBN: 978-1-492-04194-8. Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive...
MIT Press, 2016. — 802 p. — ISBN: 978-0-262-33737-3. A comprehensive introduction to neural networks and deep learning by leading researchers in this field. Written for two main target audiences: university students (undergraduate or graduate) learning about machine learning, and software engineers. This is a PDF compilation of an online book (www.deeplearningbook.org). Who...
Cambridge University Press, 2011. — 540 p. The theme of this book is that human beings possess cognitive processes that enable them to override the imperatives of past experience and to act and think in novel ways, and that these processes differ from the types of cognitive processes usually envisioned in psychological theories of learning. The capability for what I call deep...
New York: Springer, 2019. — 296 p. This book offers a timely reflection on the remarkable range of algorithms and applications that have made the area of Deep Learning (DL) so attractive and heavily researched today. Introducing the diversity of learning mechanisms in the environment of Big Data, and presenting authoritative studies in fields such as sensor design, health care,...
Packt Publishing, 2018. — 272 p. — ISBN: 1788837991. Dive deeper into neural networks and get your models trained, optimized with this quick reference guide. Deep learning has become an essential necessity to enter the world of artificial intelligence. With this book deep learning techniques will become more accessible, practical, and relevant to practicing data scientists. It...
Packt Publishing, 2018. — 272 p. — ISBN: 1788837991. Dive deeper into neural networks and get your models trained, optimized with this quick reference guide. Deep learning has become an essential necessity to enter the world of artificial intelligence. With this book deep learning techniques will become more accessible, practical, and relevant to practicing data scientists. It...
Packt Publishing, 2017. — 300 p. — ISBN: 978-1-78646-582-5. Develop deep neural networks in Theano with practical code examples for image classification, machine translation, reinforcement agents, or generative models. This book offers a complete overview of Deep Learning with Theano, a Python-based library that makes optimizing numerical expressions and deep learning models...
Packt, 2019. — 612 p. — ISBN: 9781838642709. !Code files only Explore the world of neural networks by building powerful deep learning models using the R ecosystem Key Features Get to grips with the fundamentals of deep learning and neural networks Use R 3.5 and its libraries and APIs to build deep learning models for computer vision and text processing Implement effective deep...
The MIT Press, 2019. — 296 p. — (MIT Press Essential Knowledge series). — ISBN: 9780262537551. An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones,...
Amazon Kindle Publishing, 2019. — 170 p. — ISBN: 1092562222. Build your Own Neural Network through easy-to-follow instruction and examples. Thanks this easy tutorial you’ll learn the fundamentals of Deep learning and build your very own Neural Network in Python using TensorFlow, Keras, PyTorch, and Theano. While you have the option of spending thousands of dollars on a big and...
New York: Springer, 2020. — 768 p. This book describes how neural networks operate from the mathematical perspective, having in mind that the success of the neural networks methods should not be determined by trial-and-error or luck, but by a clear mathematical analysis. The main goal of the present work is to write the ideas and concepts of neural networks, which are used...
Manning, 2019. — 396 p. The book teaches you to apply deep learning techniques to solve real-world computer vision problems. In his straightforward and accessible style, DL and CV expert Mohamed Elgendy introduces you to the concept of visual intuition — how a machine learns to understand what it sees. Then you’ll explore the DL algorithms used in different CV applications....
Packt Publishing, 2018. — 334 p. Code files only! A hands-on guide to deep learning thats filled with intuitive explanations and engaging practical examples Key Features Designed to iteratively develop the skills of Python users who dont have a data science background Covers the key foundational concepts youll need to know when building deep learning systems Full of...
No Starch Press, 2021. — 776 p. — ISBN: 978-1718500723. A richly illustrated, full-color introduction to deep learning that offers visual and conceptual explanations instead of equations. You'll learn how to use key deep learning algorithms without the need for complex math. Ever since computers began beating us at chess, they've been getting better at a wide range of human...
IOS Press, 2017. — 284 p. Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance. The aim of this book, ‘Deep Learning for Image Processing Applications’, is to offer concepts...
Singapore: Springer Singapor, 2018. — 79 p. — ISBN: 978-981-13-1444-5. This book presents recent advances in intrusion detection systems (IDSs) using state-of-the-art deep learning methods. It also provides a systematic overview of classical machine learning and the latest developments in deep learning. In particular, it discusses deep learning applications in IDSs in different...
Amazon Digital Services LLC, 2019. — 106 p. Artificial intelligence takes many shapes and forms. At this point in its evolution, machine learning and deep learning are two of the most common shapes it takes. This is primarily because we are at a point where we have discovered how to create networks of information that can actually be filtered and processed just as a normal...
Springer, 2018. — 512 p. — ISBN: 3319944622. This book covers both classical and modern models in deep learning. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between...
Packt Publishing, 2017. — 330 p. — ISBN: 178712519X. Solve different problems in modeling deep neural networks using Python, Tensorflow, and Keras with this practical guide. Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a...
Springer, 2020. — 303 p. — (Computational Synthesis and Creative Systems). — ISBN: 978-3-319-70162-2. This book is a survey and analysis of how deep learning can be used to generate musical content. The authors offer a comprehensive presentation of the foundations of deep learning techniques for music generation. They also develop a conceptual framework used to classify and...
Manning Publications, 2024. — 550 p. — ISBN: 1617296481. Shine a spotlight into the deep learning “black box”. This comprehensive and detailed guide reveals the mathematical and architectural concepts behind deep learning models, so you can customize, maintain, and explain them more effectively. Inside Math and Architectures of Deep Learning you will find: Math, theory, and...
Manning, 2018. — 325 p. Artificial intelligence has made some incredible leaps. Deep learning systems now deliver near-human speech and image recognition, not to mention machines capable of beating world champion Go masters. Deep learning applies to a widening range of problems, such as question answering, machine translation, and optical character recognition. It's behind...
Manning Publications, 2022. — 602 p. — ISBN13: 978-1617298639. Written for everyday developers, there are no complex mathematical proofs or unnecessary academic theories in Inside Deep Learning. Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems. Inside Deep Learning is a fast-paced beginner's guide...
New York: O’Reilly, 2019. — 220 p. Why Deep Learning? Contemporary Life Science Is About Data What Will You Learn? Intro to Deep Learning Linear Models Multilayer Perceptrons Training Models Regularization Hyperparameter Optimization Other Types of Models Further Reading Machine Learning with DeepChem DeepChem Datasets Training a Model to Predict Toxicity of Molecules Case...
The MIT Press, 2018. — 352 p. — ISBN13: 978-0-2620-3803-4. How deep learning — from Google Translate to driverless cars to personal cognitive assistants — is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous...
New York: Springer, 2020. — 117 p. This book provides the reader with the fundamental knowledge in the area of Deep Learning (DL) with application to visual content mining. The authors give a fresh view on Deep Learning approaches both from the point of view of image understanding and supervised Machine Learning (ML). It contains chapters which introduce theoretical and...
Packt Publishing, 2017. — 300 p. — ISBN: 978-1-78646-582-5. Develop deep neural networks in Theano with practical code examples for image classification, machine translation, reinforcement agents, or generative models. This book offers a complete overview of Deep Learning with Theano, a Python-based library that makes optimizing numerical expressions and deep learning models...
Machine Learning Mastery Pty. Ltd. — 255 p. Deep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Tap into their power in a few lines of code using Keras, the best-of-breed applied deep learning library. In this mega Ebook is written in the friendly...
Amazon Digital Services LLC, 2018. — 108 p. Deep learning is a process that widens the range of most artificial intelligence problems like speech recognition, image classification, question answering, optical character recognition, and transforming text to speech. It is true that deep learning is a complex subject to learn and understand, but it is not difficult for most...
2nd Edition. —Manning Publications, 2021. — 504 p. — ISBN: 978-1617296864. Unlock the groundbreaking advances of deep learning with this extensively revised new edition of the bestselling original. Learn directly from the creator of Keras and master practical Python deep learning techniques that are easy to apply in the real world. In Deep Learning with Python, Second Edition...
Packt Publishing, 2018. — 436 p. — ISBN: 178899745X. Build and deploy powerful neural network models using the latest Java deep learning libraries Key Features Understand DL with Java by implementing real-world projects Master implementations of various ANN models and build your own DL systems Develop applications using NLP, image classification, RL, and GPU processing Book...
Springer, 2018 (2019 Edition). — 188 p. Deep Learning and Missing Data in Engineering Systems discuss concepts and applications of artificial intelligence, specifically, deep learning. The artificial intelligence techniques that are studied include multilayer autoencoder networks and deep autoencoder networks. Also studied in this book are computational and swarm intelligence...
Amazon Digital Services LLC, 2017. — 75 p. — ISBN: 1981614060. Neural Networks and Deep Learning: Deep Learning explained to your granny – A visual introduction for beginners who want to make their own Deep Learning Neural Network (Machine Learning) Ready to crank up a neural network to get your self-driving car pick up the kids from school? Want to add 'Deep Learning’ to your...
New York: Springer, 2020. — 347 p. This book introduces readers to the fundamental concepts of deep learning and offers practical insights into how this learning paradigm supports automatic mechanisms of structural knowledge representation. It discusses a number of multilayer architectures giving rise to tangible and functionally meaningful pieces of knowledge, and shows how...
Morgan & Claypool Publisher, 2017. — 124 p. — ISBN: 978-1627057288. Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of...
Manning Publications, 2019. — 327 p. — ISBN: 1617294799. Deep Learning for Search is a practical book about how to use (deep) neural networks to help build effective search engines. This book examines several components of a search engine, providing insights on how they work and guidance on how neural networks can be used in each context. Emphasis is given to practical,...
Packt Publishing, 2018. — 284 p. Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and...
Springer, 2020. — 200 p. — (Algorithms for Intelligent Systems). — ISBN: 9789811512155. This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge...
Apress, 2019. — 702 p. — ISBN-13 (electronic): 978-1-4842-4470-8. ake a systematic approach to understanding the fundamentals of machine learning and deep learning from the ground up and how they are applied in practice. You will use this comprehensive guide for building and deploying learning models to address complex use cases while leveraging the computational resources of...
Packt Publishing, 2018. — 334 p. A hands-on guide to deep learning thats filled with intuitive explanations and engaging practical examples Key Features Designed to iteratively develop the skills of Python users who dont have a data science background Covers the key foundational concepts youll need to know when building deep learning systems Full of step-by-step exercises and...
Apress, 2017. — 169 p. — ISBN: 978-1-4842-2765-7. Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is...
Apress, 2018. — 425 p. — ISBN: 1484237897. Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish),...
Academic Press, 2019. — 269 p. — ISBN: 978-0-12-816718-2. This book delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources....
KDimensions, 2022. — 236 p. Deep Learning is the algorithm powering the current renaissance of Artificial Intelligence (AI). And the progress is not showing signs of slowing down. A McKinsey report estimates that by 2030, AI will potentially deliver $13 trillion to the global economy, or 16% of the world's current GDP. This opens up exciting career opportunities in the coming...
Apress, 2018. — 343 p. Discover the potential applications, challenges, and opportunities of deep learning from a business perspective with technical examples. These applications include image recognition, segmentation and annotation, video processing and annotation, voice recognition, intelligent personal assistants, automated translation, and autonomous vehicles. An...
Packt Publishing, 2017. — 300 p. — ISBN: 978-1-78646-582-5. Develop deep neural networks in Theano with practical code examples for image classification, machine translation, reinforcement agents, or generative models. This book offers a complete overview of Deep Learning with Theano, a Python-based library that makes optimizing numerical expressions and deep learning models...
Machine Learning Mastery, 2017. — 246 p. Welcome to Long Short-Term Memory Networks With Python. Long Short-Term Memory (LSTM) recurrent neural networks are one of the most interesting types of deep learning at the moment. They have been used to demonstrate world-class results in complex problem domains such as language translation, automatic image captioning, and text...
With contributions by Nicholas Locascio. — New York: O’Reilly Media, 2017. — 298 p. — ISBN: 978-1-491-92561-4. With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research that is paving the way for modern machine learning. This book uses exposition and examples to help you understand major concepts in this complicated...
2nd Edition. — O’Reilly Media, 2022. — 390 p. — ISBN: 978-1-492-08218-7. We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception that has powered our push toward self-driving vehicles, the ability to defeat human experts at a variety of difficult games including Go and Starcraft, and even generate essays with shockingly coherent prose....
Manning Publications, 2020. — 560 p. — ISBN: 978-1-617296-17-8. Deep learning has transformed the fields of computer vision, image processing, and natural language applications. Thanks to TensorFlow.js, now JavaScript developers can build deep learning apps without relying on Python or R. Deep Learning with JavaScript shows developers how they can bring DL technology to the...
Springer, 2019. — 109 p. This book presents the latest research on hierarchical deep learning for multi-modal sentiment analysis. Further, it analyses sentiments in Twitter blogs from both textual and visual content using hierarchical deep learning networks: hierarchical gated feedback recurrent neural networks (HGFRNNs). Several studies on deep learning have been conducted to...
2nd Edition. — Manning, 2020. — 214 p. — (MEAP Version 4). — ISBN: 9781617294433. Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. You’ll learn directly from the creator of Keras, Fran?ois Chollet, building your understanding through intuitive explanations and practical examples. Updated from the original...
Amazon Digital Services LLC, 2018. — 108 p. Deep learning is a process that widens the range of most artificial intelligence problems like speech recognition, image classification, question answering, optical character recognition, and transforming text to speech. It is true that deep learning is a complex subject to learn and understand, but it is not difficult for most...
Springer Nature Singapore Pte Ltd., 2019. — 237 p. — ISBN: 978-981-10-5151-7. This book discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its...
O’Reilly Media, Inc., 2024. — 350 p. — ISBN: 978-1-098-14839-3. Deep Learning is rapidly gaining momentum in the world of finance and trading. But for many professional traders, this sophisticated field has a reputation for being complex and difficult. This hands-on guide teaches you how to develop a deep-learning trading model from scratch using Python, and it also helps you...
No Starch Press, 2021. — 464 p. — ISBN: 9781718500747. Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects. If you’ve been curious about machine learning but didn’t know where to start, this is the book you’ve been waiting for. Focusing on the subfield of machine learning known as deep...
The MIT Press, 2023. — 541 p. — ISBN: 978-0-262-04864-4. An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice. Deep Learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date...
Independently published, 2019. — 272 p. — ISBN: 978-1793223012. New 2019 Edition! Build Deeper is a complete and practical guide that can help you take the first few steps in deep learning. It will guide you step-by-step, from understanding the basic concepts, to building your first practical application. Deep Learning is the latest iteration of AI. Although the concept itself...
Springer, 2017. — 109 p. This book proposes complex hierarchical deep architectures (HDA) for predicting bankruptcy, a topical issue for business and corporate institutions that in the past has been tackled using statistical, market-based and machine-intelligence prediction models. The HDA are formed through fuzzy rough tensor deep staking networks (FRTDSN) with structured,...
Manning, 2018. — 325 p. Artificial intelligence has made some incredible leaps. Deep learning systems now deliver near-human speech and image recognition, not to mention machines capable of beating world champion Go masters. Deep learning applies to a widening range of problems, such as question answering, machine translation, and optical character recognition. It's behind...
Amazon Digital Services LLC, 2018. — 108 p. — ASIN: B07K2Q6DXH. Deep learning is a process that widens the range of most artificial intelligence problems like speech recognition, image classification, question answering, optical character recognition, and transforming text to speech. It is true that deep learning is a complex subject to learn and understand, but it is not...
2nd Edition. — Amazon Digital Services LLC, 2018. — 770 p. The last decade and some, has witnessed some remarkable advancements in the area of Deep Learning. This area of Artificial intelligence (AI) has proliferated into many branches - Deep Belief Networks, Recurrent Neural Networks, Convolution Neural Networks, Adversorial Networks, Reinforcement Learning, Capsule Networks...
2018. — 132 p. — ISBN: 1727337964. Have you ever wanted to learn how to better use your data? Are you interested in the works of machine learning? If you answered yes to these questions, then this book is for you. Deep learning is a powerful data tool that can help improve businesses. In this book, you will learn: Neural networks Machine learning How it relates to certain...
No Starch Press, December 7th, 2021 — 344 p. — ISBN: 1718501900. Math for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and better use the deep learning toolkits. With Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning. You’ll work...
No Starch Press, 2022 — 347 p. Math for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and better use the deep learning toolkits. With Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning. You’ll work through Python examples to learn key...
Canada: John Wiley & Sons, Inc, 2019. — 442 p. — ISBN: 978-1-119-54304-6. Take a deep dive into deep learning. Deep learning provides the means for discerning patterns in the data that drive online business and social media outlets. Deep Learning for Dummies gives you the information you need to take the mystery out of the topic — and all of the underlying technologies...
O’Reilly, 2018. - 220 p. - ISBN: 149199584X Deep learning doesnt have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook, youll learn how to solve deep-learning...
Chapman and Hall/CRC, 2017. — 364 p. — (Machine Learning & Pattern Recognition). — ISBN: 978-1138626782. Introduction to Machine Learning with Applications in Information Security provides a class-tested introduction to a wide variety of machine learning algorithms, reinforced through realistic applications. The book is accessible and doesn’t prove theorems, or otherwise dwell...
New York: Springer, 2022. — 210 p. This textbook tackles the problem of formulating AI systems by combining probabilistic modeling and deep learning. Moreover, it goes beyond typical predictive modeling and brings together supervised learning and unsupervised learning. The resulting paradigm, called deep generative modeling, utilizes the generative perspective on perceiving the...
2018. — 204 p. Artificial Intelligence is changing our lives, and solutions based on Deep Learning are leading this transformation. Deep Learning is now of major interest to private companies, since it can be applied to many areas of activity. But getting started in this technology is not an easy task. Many enthusiastic professionals in the field of Deep Learning have...
Manning Publications Co., 2019. — 335 p. — ISBN: 978-1617293702. Artificial Intelligence is one of the most exciting technologies of the century, and Deep Learning is in many ways the 'brain' behind some of the world's smartest Artificial Intelligence systems out there. Loosely based on neuron behavior inside of human brains, these systems are rapidly catching up with the...
Springer, 2020. — 159 p. — (Studies in Big Data 57). — ISBN: 978-981-13-6793-9. This book introduces readers to both basic and advanced concepts in deep network models. It covers state-of-the-art deep architectures that many researchers are currently using to overcome the limitations of the traditional artificial neural networks. Various deep architecture models and their...
Princeton: LN, 2023. — 227 p. Basic Setup and some math notions. List of useful math facts. Basics of Optimization. Gradient descent (GD). Stochastic gradient descent (SGD). Accelerated Gradient Descent. Running time: Learning Rates and Update Directions. Convergence rates under smoothness conditions. Correspondence of theory with practice. Note on overparametrized linear...
Boca Raton: CRC Press, 2024. — 313 p. Deep Learning for Multimedia Processing Applications is a comprehensive guide that explores the revolutionary impact of Deep Learning techniques in the field of multimedia processing. Written for a wide range of readers, from students to professionals, this book offers a concise and accessible overview of the application of Deep Learning in...
Boca Raton: CRC Press, 2023. — 481 p. Deep Learning for Multimedia Processing Applications is a comprehensive guide that explores the revolutionary impact of deep learning techniques in the field of multimedia processing. Written for a wide range of readers, from students to professionals, this book offers a concise and accessible overview of the application of deep learning in...
Springer, 2024. — 656 p. This book offers a comprehensive introduction to the central ideas that underpin Deep Learning. It is intended both for newcomers to machine learning and for those already experienced in the field. Covering key concepts relating to contemporary architectures and techniques, this essential book equips readers with a robust foundation for potential future...
IOP Publishing, 2024. — 365 p. — ISBN: 978-0-7503-6242-9. This book, together with the accompanying Python codes, provides a thorough guide for mastering advanced computer vision techniques for image processing by using the open-source machine learning framework PyTorch. Known for its user-friendly interface and Python programming style, PyTorch is accessible and one of the...
Packt Publishing, 2024. — 274 p. — ISBN: 978-1805129233. Learn how to deal with time series data and how to model it using deep learning and take your skills to the next level by mastering PyTorch using different Python recipes. Key Features Learn the fundamentals of time series analysis and how to model time series data using deep learning. Explore the world of deep learning...
Manning Publications, 2024. — 553 p. ISBN: 9781617296482. Shine a spotlight into the deep learning “black box”. This comprehensive and detailed guide reveals the mathematical and architectural concepts behind deep learning models so that you can customize, maintain, and explain them more effectively. Inside Math and Architectures of Deep Learning you will find: Math, theory,...
Manning Publications, 2018. — 392 p. — ISBN13: 978-1617295546. Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples. Machine learning has made remarkable progress in recent years. Deep-learning systems now...
Pearson Education, 2022. — 747 p. — ISBN: 978-0-13-747035-8. NVIDIA's Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results "To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals." -- From the foreword by Dr. Anima...
O’Reilly Media, 2019. — 55 p. — ISBN: 1492041947. Generative modeling is one of the hottest topics in artificial intelligence (AI). Recent advances in the field have shown how it’s possible to teach a machine to excel at human endeavors — such as drawing, composing music, and completing tasks — by generating an understanding of how its actions affect its environment. With this...
MIT Press, 2016. — 802 p. — ISBN: 978-0-262-33737-3. A comprehensive introduction to neural networks and deep learning by leading researchers in this field. Written for two main target audiences: university students (undergraduate or graduate) learning about machine learning, and software engineers. This is a PDF compilation of an online book (www.deeplearningbook.org). Who...
Apress, 2017. — 169 p. — ISBN: 978-1-4842-2765-7. Code files only! Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of...
O’Reilly Media, 2019. — 620 p. — ISBN: 978-1-492-03486-5. Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile,...
O’Reilly, 2024. — 448 p. — ISBN: 978-1-098-14528-6. Bringing a deep-learning project into production at scale is quite challenging. To successfully scale your project, a foundational understanding of full-stack deep learning, including the knowledge that lies at the intersection of hardware, software, data, and algorithms, is required. This book illustrates complex concepts of...
O’Reilly Media, 2017. — 538 p. — ASIN B074D5YF1D. Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning — especially deep neural networks — make a real difference in your organization? This hands-on guide not only provides the most practical information available on the...
Amazon Digital Services LLC, 2019. — 89 p. — ASIN B07GTT616W. Want to learn deep learning and AI, but hate math? This book is an experiment for me. After years of teaching successful deep learning and machine learning courses online, I’ve come to notice a few patterns. One of them is that a large subset of students just RUN AWAY at the sight of math. This is somewhat...
O’Reilly, 2019. — 220 p. — ISBN13: 978-1-492-03983-9 Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields....
Cambridge: Cambridge University Press, 2022. — 472 p. — ISBN: 1316519333. This textbook establishes a theoretical framework for understanding deep learning models of practical relevance. With an approach that borrows from theoretical physics , Roberts and Yaida provide clear and pedagogical explanations of how realistic deep neural networks work. To make results from the...
New York: Springer, 2020. — 144 p. This stimulating text/reference presents a philosophical exploration of the conceptual foundations of deep learning, presenting enlightening perspectives that encompass such diverse disciplines as computer science, mathematics, logic, psychology, and cognitive science. The text also highlights select topics from the fascinating history of this...
Manning Publications Co., 2023. — 362 p. — ISBN: 978-1633439863. A vital guide to building the platforms and systems that bring deep learning models to production. In Designing Deep Learning Systems you will learn how to: Transfer your software development skills to deep learning systems. Recognize and solve common engineering challenges for deep learning systems. Understand...
Springer, 2020. — 117 p. — ISBN: 978-3-030-34375-0. This book provides the reader with the fundamental knowledge in the area of deep learning with application to visual content mining. The authors give a fresh view on Deep learning approaches both from the point of view of image understanding and supervised machine learning. It contains chapters which introduce theoretical and...
2nd Edition. — Manning Publications, 2021. — 504 p. — ISBN: 978-1617296864. Unlock the groundbreaking advances of deep learning with this extensively revised new edition of the best-selling original. Learn directly from the creator of Keras and master practical Python deep-learning techniques that are easy to apply in the real world. In Deep Learning with Python, Second Edition...
Amazon Digital Services LLC, 2018. — 98 p. — ASIN: B07K2Q6DXH. Deep learning is a process that widens the range of most artificial intelligence problems like speech recognition, image classification, question answering, optical character recognition, and transforming text to speech. It is true that deep learning is a complex subject to learn and understand, but it is not...
Packt Publishing, 2018. — 271 p. — ISBN: 978-1-78588-036-0. Get to grips with the essentials of deep learning by leveraging the power of Python Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training...
Cambridge: Cambridge University Press, 2022. - 361 p. - ISBN: 1108835082. The Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies. prepared them for careers in deep learning, and machine learning. artificial intelligence in top companies in industry and academia. The...
PB Publications, 2023. — 237 p. — ISBN: 978-93-5551-105-8. A step-by-step guide to getting started with Machine Learning. Key Features Understand different types of Machine Learning like Supervised, Unsupervised, Semi-supervised, and Reinforcement learning. Learn how to implement Machine Learning algorithms effectively and efficiently. Get familiar with the various libraries &...
Manning, 2020. — 297 p. — ISBN: 9781617296079. Probabilistic Deep Learning with Python shows how probabilistic deep learning models gives readers the tools to identify and account for uncertainty and potential errors in their results. Starting by applying the underlying maximum likelihood principle of curve fitting to deep learning, readers will move on to using the...
Amazon Digital Services LLC, 2020. — 148 p. — ASIN B083S1DDB7. Learn to create inventive programs on your Machine Learning&Deep Learning and Python — with no programming experience required. Discover how to configure, write Python scripts, create user-friendly GUIs.Projects include a object detection by find object with camera, tracking motion. Hands-on Deep Learning&Machine...
Addison-Wesley Professional, 2021. — 752 p. — ISBN: 978-0137470358. NVIDIA's Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results "To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals." -- From the foreword by Dr....
Manning Publications Co, 2021. — 471 p. — ISBN: 9781617298264. Discover best practices, reproducible architectures, and design patterns to help guide deep learning models from the lab into production. In Deep Learning Patterns and Practices you will learn: The internal functioning of modern convolutional neural networks. Procedural reuse design pattern for CNN architectures....
Apress, 2022. — 395 p. — ISBN13: 978-1-4842-8148-2. Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learning approaches and provides techniques to facilitate your deep learning model development. The first chapters...
Cambridge: Cambridge University Press, 2023. - 492 p. - ISBN: 1316516784. In recent years the development of new classification and regression algorithms based on deep learning has led to a revolution in the fields of artificial intelligence, machine learning, and data analysis. The development of a theoretical foundation to guarantee the success of these algorithms constitutes...
O’Reilly Media, Inc., 2024. — 350 p. — ISBN: 978-1-098-14839-3. Deep Learning is rapidly gaining momentum in the world of finance and trading. But for many professional traders, this sophisticated field has a reputation for being complex and difficult. This hands-on guide teaches you how to develop a deep-learning trading model from scratch using Python, and it also helps you...
2nd edition. — Apress, 2021. — 316 p. — ISBN: 978-1484253632. Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a...
Addison-Wesley Professional, 2020. — 415 p. — (Addison-Wesley Data & Analytics Series). — ISBN13: 978-0-13-511669-2. Deep learning is one of today’s hottest fields. This approach to machine learning is achieving breakthrough results in some of today’s highest profile applications, in organizations ranging from Google to Tesla, Facebook to Apple. Thousands of technical...
Apress, 2018. — 219 p. Discover the essential building blocks of the most common forms of deep belief networks. At each step this book provides intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with...
Apress, 2019. — 294 p. — ISBN: 1484249755. Develop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In Advanced Applied Deep Learning, you will study advanced topics on CNN and object detection using Keras and TensorFlow. Along the...
John Wiley & Sons, Inc, 2019. — 442 p. — ISBN: 978-1-119-54304-6. Take a deep dive into deep learning. Deep learning provides the means for discerning patterns in the data that drive online business and social media outlets. Deep Learning for Dummies gives you the information you need to take the mystery out of the topic — and all of the underlying technologies associated with...
Amazon Digital Services LLC, 2017. — 75 p. — ISBN: 1981614060. Neural Networks and Deep Learning: Deep Learning explained to your granny – A visual introduction for beginners who want to make their own Deep Learning Neural Network (Machine Learning) Ready to crank up a neural network to get your self-driving car pick up the kids from school? Want to add 'Deep Learning’ to your...
CRC Press, 2023. — 276 p. — ISBN: 978-1-032-34924-4. Object Detection with Deep Learning Models discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and...
The MIT Press, 2023. — 541 p. An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice. Deep Learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject,...
New York: Manning Publications, 2020. — 100 p. Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems. In Inside Deep Learning, you will learn how to: Implement deep learning with PyTorch Select the right deep learning components Train and evaluate a deep learning model Fine tune deep learning models to...
Wiley-IEEE Press, 2024. — 259 p. Discover how to train Deep Learning models by learning how to build real Deep Learning software libraries and verification software! The study of Deep Learning and Artificial Neural Networks (ANN) is a significant subfield of artificial intelligence (AI) that can be found within numerous fields: medicine, law, financial services, and science,...
Independently published, 2021. — 390 p. — ISBN: 979-8721791499. This book is not only for programmers and IT professionals but also for businesspeople who are looking forward to boosting their average sales and customer experience. This book contains all the relevant topics that you’ll want to know about deep learning neural networks. You will learn some amazing facts about the...
Apress Media, LLC., 2023. — 870 p. — ISBN-13: 978-1-4842-8691-3. Deep learning is one of the most powerful tools in the modern Artificial Intelligence landscape. While having been predominantly applied to highly specialized image, text, and signal datasets, this book synthesizes and presents novel deep-learning approaches to a seemingly unlikely domain – tabular data. Whether...
2019. — 536 p. Artificial Intelligence (AI) especially Deep Learning (DL) has made tremendous progress in recent years. It start to spread in many areas, such as: image classification, voice recognition, text generation, language translation etc. As time goes by, it becomes apparent that deep learning will stay in the mainstream. As a technology people, it is time to keep...
Manning Publications, 2020. — 384 p. — ISBN: 9781617295430 Deep Reinforcement Learning in Action teaches you how to program agents that learn and improve based on direct feedback from their environment. You’ll build networks with the popular PyTorch deep learning framework to explore reinforcement learning algorithms ranging from Deep Q-Networks to Policy Gradients methods to...
AI Publishing LLC, 2020. — 293 p. — ISBN: B08959G3H8. Artificial intelligence is the rage today! While you may find it difficult to understand the most recent advancements in AI, it simply boils down to two most celebrated developments: Machine Learning and Deep Learning. In 2020, Deep Learning is leagues ahead because of its supremacy when it comes to accuracy, especially when...
Berlin: de Gruyter, 2023. — 134 p. The goal of this book is to provide a mathematical perspective on some key elements of the so-called Deep Neural Networks (DNNs). Much of the interest in Deep Learning has focused on the implementation of DNN-based algorithms. Our hope is that this compact textbook will offer a complementary point of view that emphasizes the underlying...
Springer, 2020. — 303 p. — (Computational Synthesis and Creative Systems). — ISBN: 978-3-319-70162-2. This book is a survey and analysis of how deep learning can be used to generate musical content. The authors offer a comprehensive presentation of the foundations of deep learning techniques for music generation. They also develop a conceptual framework used to classify and...
Machine Learning Mastery, 2018. — 575 p. Deep learning neural networks have become easy to define and fit, but are still hard to configure. Discover exactly how to improve the performance of deep learning neural network models on your predictive modeling projects. With clear explanations, standard Python libraries, and step-by-step tutorial lessons, you’ll discover how to...
Packt Publishing, 2022. — 470 p. — ISBN: 978-1-80461-544-7. Learn concepts, methodologies, and applications of deep learning for building predictive models from complex genomics data sets to overcome challenges in the life sciences and biotechnology industries. Key Features - Apply deep learning algorithms to solve real-world problems in the field of genomics. - Extract...
Manning, 2021. — 480 p. — ISBN: 9781617296192. The definitive M&E price book with additions to the measured works, updates to approximate estimating and new engineering features. Spon's Mechanical and Electrical Services Price Book 2021 continues to be the most comprehensive and best annual services engineering price book currently available, providing detailed pricing...
Birmingham: Packt Publishing, 2023. - 349 p. - ISBN: 180324013X. Achieve TensorFlow certification with this comprehensive guide covering all exam topics using a hands-on, step-by-step approach — perfect for aspiring TensorFlow developers. Key Features Build real-world computer vision, natural language, and time series applications. Learn how to overcome issues such as...
Arcler Press, 2022. — 412 p. This book covers different topics from deep learning algorithms, methods and approaches for deep learning, deep learning applications in biology, deep learning applications in medicine, and deep learning applications in pattern recognition systems. Section 1 focuses on methods and approaches for deep learning, describing advancements in deep...
CRC Press, 2023. — 208 p. — eBook ISBN: 978-0-429-32125-2. Deep learning is an important element of artificial intelligence, especially in applications such as image classification in which various architectures of neural networks, e.g., convolutional neural networks, have yielded reliable results. This book introduces deep learning for time series analysis, particularly for...
BPB Publications, 2023. — 477 p. — ISBN: 978-93-5551-194-2. Mathematical Codebook to Navigate Through the Fast-changing AI Landscape. Key Features - Access to industry-recognized AI methodology and deep learning mathematics with simple-to-understand examples. - Encompasses MDP Modeling, the Bellman Equation, Auto-regressive Models, BERT, and Transformers. - Detailed,...
Academic Press/Elsevier, 2023. — 270 p. — (Handbook of Statistics 48). — ISBN: 9780443184314. Deep Learning, Volume 48 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics, including Generative Adversarial Networks for Biometric Synthesis, Data Science and Pattern...
SciTech Publishing, 2021. — 897 p. — ISBN: 978178561853. Novel deep learning approaches are achieving state-of-the-art accuracy in the area of radar target recognition, enabling applications beyond the scope of human-level performance. This book provides an introduction to the unique aspects of machine learning for radar signal processing that any scientist or engineer seeking...
SciTech Publishing, 2021. — 419 p.— ISBN: 1785618520, 9781785618529. Novel deep learning approaches are achieving state-of-the-art accuracy in the area of radar target recognition, enabling applications beyond the scope of human-level performance. This book provides an introduction to the unique aspects of machine learning for radar signal processing that any scientist or...
Apress Media, LLC., 2022-12-31. — 239 p. — ISBN13: 978-1-4842-8586-2. Data is the indispensable fuel that drives the decision-making of everything from governments, to major corporations, to sports teams. Its value is almost beyond measure. But what if that data is either unavailable or problematic to access? That’s where synthetic data comes in. This book will show you how to...
Springer Nature Singapore Pte Ltd., 2019. — 237 p. — ISBN: 978-981-10-5152-4 (eBook). This book discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and...
Springer Nature Singapore Pte Ltd., 2019. — 237 p. — ISBN: 978-981-10-5151-7. This book discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its...
Springer, 2024. — 413 p. This book provides a systematic study of the security of Deep Learning. With its powerful learning ability, Deep Learning is widely used in CV, FL, GNN, RL, and other scenarios. However, during the process of application, researchers have revealed that Deep Learning is vulnerable to malicious attacks, which will lead to unpredictable consequences. Take...
Springer, 2023. — 432 p. — ISBN: 3031328787. This book provides a conceptual understanding of deep learning algorithms. The book consists of four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for...
The MIT Press, 2019. — 196 p. — (MIT Press Essential Knowledge series). — ISBN: 978-0262537551. An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones,...
BPB Publications, 2024. — 544 p. — ISBN: 978-93-55515-391. A hands-on guide to building and deploying Deep Learning models with Python. Key Features: Acquire the skills to perform exploratory data analysis, uncover insights, and preprocess data for Deep Learning tasks. Build and train various types of neural networks, including Convolutional Neural Networks (CNNs) and Recurrent...
No Starch Press, 2021. — 464 p. — ISBN: 9781718500747. Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your DL projects. If you’ve been curious about machine learning but didn’t know where to start, this is the book you’ve been waiting for. Focusing on the subfield of machine learning known as deep...
CRC Press, 2025. — 408 p. — ISBN: 978-1-032-72212-2. May 11, 1997, was a watershed moment in the history of artificial intelligence (AI): the IBM supercomputer chess engine, Deep Blue, beat the world Chess champion, Garry Kasparov. It was the first time a machine had triumphed over a human player in a Chess tournament. Fast forward 19 years to May 9, 2016, DeepMind’s AlphaGo...
Springer Nature, 2019. — 452 p. — ISBN: 978-3-319-42999-1. This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly...
Cambridge University Press, 2021. — 339 p. — ISBN: 978-1-108-83174-1. Deep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established methods from...
Apress, 2022. - 397 p. - ISBN: 1484280199. Understand how neural networks work and learn how to implement them using TensorFlow 2.0 and Keras. This new edition focuses on the fundamental concepts and at the same time on practical aspects of implementing neural networks and deep learning for your research projects. This book is designed so that you can focus on the parts you are...
Apress, 2018. — 425 p. — ISBN13: 978-1-4842-3790-8. Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You'll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and...
John Wiley & Sons, Inc, 2019. — 442 p. — ISBN: 978-1-119-54304-6. Take a deep dive into deep learning. Deep learning provides the means for discerning patterns in the data that drive online business and social media outlets. Deep Learning for Dummies gives you the information you need to take the mystery out of the topic — and all of the underlying technologies associated with...
Amazon Digital Services LLC, 2017. — 75 p. — ISBN: 1981614060. Neural Networks and Deep Learning: Deep Learning explained to your granny – A visual introduction for beginners who want to make their own Deep Learning Neural Network (Machine Learning) Ready to crank up a neural network to get your self-driving car pick up the kids from school? Want to add 'Deep Learning’ to your...
Apress Media, LLC., 2023. — 672 p. — ISBN13: 978-1-4842-8930-3. This book builds upon the foundations established in its first edition, with updated chapters and the latest code implementations to bring it up-to-date with Tensorflow 2.0. Pro Deep Learning with TensorFlow 2.0 begins with the mathematical and core technical foundations of deep learning. Next, you will learn about...
Independently Published, 2024. — 405 p. Unlock the full potential of deep learning with "Deep Learning Deployment with ONNX and CUDA", your comprehensive guide to deploying high-performance AI models across diverse environments. This expertly crafted book navigates the intricate landscape of deep learning deployment, offering in-depth coverage of the pivotal technologies ONNX...
Springer.English .2022 .ISBN: 9811906378 Deep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously considered to be very...
Amazon Digital Services LLC, 2019. — 89 p. — ASIN B07GTT616W. Want to learn deep learning and AI, but hate math? This book is an experiment for me. After years of teaching successful deep learning and machine learning courses online, I’ve come to notice a few patterns. One of them is that a large subset of students just RUN AWAY at the sight of math. This is somewhat...
O’Reilly, 2019. — 253 p. — ISBN13: 978-1-492-03983-9 Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields....
Independently published, 2023. — 428 p. "It is like a voyage of discovery, seeking not for new territory but new knowledge. It should appeal to those with a good sense of adventure," Dr. Frederick Sanger. I hope every reader enjoys this voyage in deep learning and finds their adventure. Think of deep learning as an art of cooking. One way to cook is to follow a recipe. But when...
Springer Cham, 2023. — 514 p. — (Studies in Big Data, volume 134) — eBook ISBN: 978-3-031-40688-1. Introduces machine and deep learning approaches for solving challenging problems. Collects the latest technological innovations and models related to deep learning. Includes representative applications and case studies using cutting-edge technologies. In recent years, significant...
Morgan & Claypool, 2021. - 265 p. - ISBN: 1681739682 This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications. The exponential growth in computational power is slowing at a time when the amount of compute consumed by state-of-the-art deep learning (DL)...
Manning Publications Co., 2025. — 504 p. — ISBN: 978-1633438545. Business runs on tabular data in databases, spreadsheets, and logs. Crunch that data using deep learning, gradient boosting, and other machine learning techniques. Machine Learning for Tabular Data teaches you to train insightful machine learning models on common tabular business data sources such as spreadsheets,...
Manning Publications, 2024. — 410 p. — (Final). — ISBN: 9781633438880. Embark on a journey into the world of JAX, a cutting-edge library that’s revolutionizing deep learning and high-performance computing. In this opening part of JAX for Deep Learning, we lay the groundwork for understanding why JAX is a pivotal tool in the ever-evolving landscape of machine learning...
Wiley-Scrivener, 2024. — 496 p. — ISBN: 978-1394214303. This book provides a comprehensive overview of current research and developments in the field of deep learning models for stock market forecasting in the developed and developing worlds. The book delves into the realm of deep learning and embraces the challenges, opportunities, and transformation of stock market analysis....
Vivek S. Sharma, Shubham Mahajan, Anand Nayyar, Amit Kant Pandit (Editor). — CRC Press, 2025. — 390 p. — ISBN: 978-1032931999. Unlock the transformative potential of deep learning in your professional and academic endeavors with Deep Learning in Engineering, Energy, and Finance: Principals and Applications. This comprehensive guide seamlessly bridges the gap between theoretical...
Palm Bay: CRC Press/Apple Academic Press, 2022. — 289 p. An enlightening amalgamation of deep learning concepts with visual computing and signal processing applications, this new volume covers the fundamentals and advanced topics in designing and deploying techniques using deep architectures and their application in visual computing and signal processing. The volume first lays...
Manning Publications, 2023. - 272 p. - ISBN: 163343902X. Guide machine learning projects from design to production with the techniques in this unique project management guide. No ML skills are required! In Managing Machine Learning Projects you’ll learn essential machine learning project management techniques, including: Understanding an ML project’s requirements. Setting up...
Nova Science Publishers, 2021. — 222 p. List of Reviewers Application of Deep Learning in Recommendation System Abstract Background and Terminologies Recommendation System Deep Learning Techniques Autoencoder Recurrent Neural Network Convolution Neural Network Restricted Boltzmann Machine Application of Deep Learning in Recommendation System Collaborative Filtering...
John Wiley & Sons, 2023. — 544 p. — ISBN: 978-1-119-84502-7. A concise and practical exploration of key topics and applications in data science. In Deep Learning: From Big Data to Artificial Intelligence with R, expert researcher Dr. Stéphane Tufféry delivers an insightful discussion of the applications of deep learning and big data that focuses on practical instructions on...
Bentham Science Publishers, 2023. — 225 p. — eBook ISBN: 978-981-5136-98-2. Numerical Machine Learning is a simple textbook on machine learning that bridges the gap between mathematics theory and practice. The book uses numerical examples with small datasets and simple Python codes to provide a complete walkthrough of the underlying mathematical steps of seven commonly used...
New York: Springer, 2022. — 338 p. The focus of this book is on providing students with insights into geometry that can help them understand deep learning from a unified perspective. Rather than describing deep learning as an implementation technique, as is usually the case in many existing deep learning books, here, deep learning is explained as an ultimate form of signal...
IGI Global, 2023. — 326 p. — ISBN: 978-1668469101. Social media platforms are one of the main generators of textual data where people around the world share their daily life experiences and information with online society. The social, personal, and professional lives of people on these social networking sites generate not only a huge amount of data but also open doors for...
Springer, 2022. — 271 p. — ISBN: 978-3-030-75854-7. This book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications. The book discusses significant issues relating to deep learning in data analytics. Further in-depth reading can be done from the detailed bibliography presented...
Elsevier, 2021. — 307 p. Deep learning (DL) is a method of machine learning, running over artificial neural networks, that uses multiple layers to extract high-level features from large amounts of raw data. DL methods apply levels of learning to transform input data into more abstract and composite information. Handbook for Deep Learning in Biomedical Engineering: Techniques...
Cambridge: Cambridge University Press, 2021. — 387 p. This is the first rigorous, self-contained treatment of the theory of deep learning. Starting with the foundations of the theory and building it up, is essential reading for any scientists, instructors, and students interested in artificial intelligence and deep learning. It guides how to think about scientific questions and...
Wiley-Scrivener, 2023. — 255 p. Research into Deep Learning has come a long way across multiple domains, such as healthcare, marketing, banking, manufacturing, education, and so on. Notable applications within these domains are trending, like visual recognition, fraud detection, virtual assistance, NLP, etc. Deep Learning models are used to implement these applications. Those...
Apress, 2024. - 372 p. - ISBN: 9798868810343. This book discusses deep learning, from its fundamental principles to its practical applications, with hands-on exercises and coding. It focuses on deep learning techniques and shows how to apply them across various practical scenarios. The book begins with an introduction to the core concepts of deep learning. It delves into...
Manning Publications Co., 2025. — 392 p. — ISBN: 978-1617299056. A hands-on guide to powerful graph-based deep learning models. In Graph Neural Networks in Action, you will learn how to: Train and deploy a graph neural network. Generate node embeddings. Use GNNs at scale for very large datasets. Build a graph data pipeline. Create a graph data schema. Understand the taxonomy of...
The article, IEEE Signal Processing Magazine, Digital Object Identifier, 11.07.2017, 18 - 42 p. Many scientific fields study data with an underlying structure that is non-Euclidean. Some examples include social networks in computational social sciences, sensor networks in communications, functional networks in brain imaging, regulatory networks in genetics, and meshed surfaces...
2nd Edition. — O’Reilly Media, 2022. — 387 p. — ISBN: 978-1-492-08218-7. We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception that has powered our push toward self-driving vehicles, the ability to defeat human experts at a variety of difficult games including Go and Starcraft, and even generate essays with shockingly coherent prose....
2nd Edition. — O’Reilly Media, 2022. — 387 p. — ISBN: 978-1-492-08218-7. We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception that has powered our push toward self-driving vehicles, the ability to defeat human experts at a variety of difficult games including Go and Starcraft, and even generate essays with shockingly coherent prose....
Manning Publications, 2020. — 560 p. — ISBN: 978-1-617296-17-8. Code files only! Deep learning has transformed the fields of computer vision, image processing, and natural language applications. Thanks to TensorFlow.js, now JavaScript developers can build deep learning apps without relying on Python or R. Deep Learning with JavaScript shows developers how they can bring DL...
Wiley, 2021. — 435 p. — ISBN: 9781119646143. Deep Learning for the Earth Sciences: Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link...
Wiley-Scrivener, 2023. — 472 p. — (Artificial Intelligence and Soft Computing for Industrial Transformation). In-depth analysis of Deep Learning-based cyber-IoT systems and security which will be the industry leader for the next ten years. Deep Learning (also known as deep structured learning) is part of a broader family of Machine Learning methods based on artificial neural...
Manning Publications, 2024. — 552 p. — ISBN: 978-1617296482. Shine a spotlight into the deep learning “black box”. This comprehensive and detailed guide reveals the mathematical and architectural concepts behind deep learning models, so you can customize, maintain, and explain them more effectively. Inside Math and Architectures of Deep Learning you will find: Math, theory, and...
Manning Publications, 2018. — 392 p. — ISBN13: 978-1617295546. Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples. Machine learning has made remarkable progress in recent years. Deep-learning systems now...
BPB Publications, 2019. — 357 p. — ISBN: 978-93-88511-018. Get hands on with Kelp.Net, Microsoft’s latest Deep Learning framework Key Features Deep Learning Basics The ultimate Kelp.Net reference guide Develop state of the art deep learning applications C# deep learning code Develop advanced deep learning models with minimal code Develop your own advanced deep learning models...
LazyProgrammer, 2016. — 59 p. This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural...
Quantum Technologies, 2023. — 276 p. Dive into the world of Generative Deep Learning with Python, mastering GANs, VAEs, & autoregressive models through projects & advanced topics. Gain practical skills & theoretical knowledge to create groundbreaking AI applications. Key Features: Comprehensive coverage of deep learning and generative models. In-depth exploration of GANs, VAEs,...
LazyProgrammer, 2016. — 71 p. Deep learning is making waves. At the time of this writing (March 2016), Google’s AlghaGo program just beat 9-dan professional Go player Lee Sedol at the game of Go, a Chinese board game. Experts in the field of Artificial Intelligence thought we were 10 years away from achieving a victory against a top professional Go player, but progress seems to...
Packt, 2018. — 271 p. — ISBN: 1785880365. Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train,...
Manning Publications, 2020. — 300 p. — ISBN: 978-1617296079. About the Technology Probabilistic deep learning models are better suited to dealing with the noise and uncertainty of real world data — a crucial factor for self-driving cars, scientific results, financial industries, and other accuracy-critical applications. By utilizing probabilistic techniques, deep learning...
Wiley-IEEE Press, 2023. — 274 p. — (IEEE Press Series on Control Systems Theory and Applications). — ISBN: 9781119808572. Model-Based Reinforcement Learning Explore a comprehensive and practical approach to reinforcement learning. Reinforcement learning is an essential paradigm of machine learning, wherein an intelligent agent performs actions that ensure optimal behavior from...
Manning Publications Co, 2021. — 471 p. — ISBN: 9781617298264. Discover best practices, reproducible architectures, and design patterns to help guide deep learning models from the lab into production. In Deep Learning Patterns and Practices you will learn: The internal functioning of modern convolutional neural networks. Procedural reuse design pattern for CNN architectures....
2nd Edition. — O’Reilly Media, Inc., 2023. — 453 p. — ISBN: 978-1-098-13418-1. Generative modeling is one of the hottest topics in artificial intelligence. Recent advances in the field have shown how it’s possible to teach a machine to excel at human endeavors – such as drawing, composing music, and completing tasks – by generating an understanding of how its actions affect its...
Applied Data Science Partners Ltd, 2019. — 330 p. — ISBN: 978-1-492-04194-8. Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive...
The MIT Press, 2023. — 440 p. — ISBN: 978-0-262-54637-9. A highly accessible, step-by-step introduction to Deep Learning, written in an engaging, question-and-answer style. The Little Learner introduces Deep Learning from the bottom up, inviting students to learn by doing. With the characteristic humor and Socratic approach of classroom favorites The Little Schemer and The...
Springer Singapore, 2024. — 201 p. — (Transactions on Computer Systems and Networks (TCSN)). — eBook ISBN: 978-981-99-9672-8. Offers a special chapter devoted to performance evaluation of deep learning algorithms. Demonstrates illustrative colorful block diagrams, figures, and full code examples to present the ideas involved. Gives content geared for both professionals and...
CRC Press, 2022. — 219 p. — ISBN: 978-0-367-45658-0. Deep Learning in Practice helps you learn how to develop and optimize a model for your projects using Deep Learning (DL) methods and architectures. Key features Demonstrates a quick review of Python, NumPy, and TensorFlow fundamentals. Explains and provides examples of deploying TensorFlow and Keras in several projects....
No Starch Press, 2021. — 776 p. — ISBN: 978-1718500723. A richly-illustrated, full-color introduction to deep learning that offers visual and conceptual explanations instead of equations. You'll learn how to use key deep learning algorithms without the need for complex math. Ever since computers began beating us at chess, they've been getting better at a wide range of human...
MIT Press, 2016. — 802 p. — ISBN 978-0-262-33737-3. A comprehensive introduction to neural networks and deep learning by leading researchers of this field. Written for two main target audiences: university students (undergraduate or graduate) learning about machine learning, and software engineers. Who Should Read This Book? Historical Trends in Deep Learning. Applied Math and...
Apress Media LLC, 2023. — 396 p. — ISBN-13: 978-1-4842-8148-2. Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learning approaches and provides techniques to facilitate your deep learning model development. After...
Nova Science Publishers, Inc., 2023. — 140 p. Deep Learning has been developed for more than 10 years. Many novel models are proposed. Among others, the attention models have greatly impacted the Deep Learning area. Similar to the attention mechanism of human beings, the attention mechanism improves the performance of many Deep Learning models based on its discovery of...
BPB Publications, 2023. — 301 p. — ISBN: 978-93-55518-057. A step-by-step guide that will teach you how to deploy TinyML on microcontrollers. Key Features: Deploy machine learning models on edge devices with ease. Leverage pre-built AI models and deploy them without writing any code. Create smart and efficient IoT solutions with TinyML. Description: TinyML, or Tiny Machine...
Apress Media, LLC., 2022-12-31. — 239 p. — ISBN13: 978-1-4842-8587-9. Data is the indispensable fuel that drives the decision-making of everything from governments, to major corporations, to sports teams. Its value is almost beyond measure. But what if that data is either unavailable or problematic to access? That’s where synthetic data comes in. This book will show you how to...
Springer. 2023. — 249 p. Unlike most available sources that focus on deep neural network (DNN) inference, this book provides readers with a single-source reference on the needs, requirements, and challenges involved with on-device, DNN training semiconductors, and SoC design. The authors include coverage of the trends and history surrounding the development of on-device DNN...
Cybellium Ltd, 2023. — 594 p. Unleash the Power of Neural Networks for Intelligent Solutions. In the landscape of Artificial Intelligence and Machine Learning, Deep Learning stands as a revolutionary force that is shaping the future of technology. "Mastering Deep Learning" is your ultimate guide to comprehending and harnessing the potential of deep neural networks, empowering...
Academic Press, 2022. — 638 p. Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end...
IGI Global, 2023. — 324. — (Advances in Finance, Accounting, and Economics (AFAE) Book Series). — ISBN: 9781668444849. The advancements in Artificial Intelligence and Machine Learning have significantly affected the way financial services are offered and adopted today. Important financial decisions such as investment decision-making, macroeconomic analysis, and credit...
Springer Nature Singapore Pte Ltd., 2019. — 237 p. — ISBN: 978-981-10-5152-4 (eBook). This book discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and...
Independently published, 2022. — 585 p. Composed for everyday programmers, there are no complicated mathematical proofs or unnecessary academic concepts in Inside Deep Discovering . Trip with the theory and technique of modern deep learning, and also use ingenious methods to solve daily information issues. Inside Deep Understanding is a busy novice's guide to solving common...
Independently published, 2022. — 473 p. Computer system vision is the main to several groundbreaking innovations, consisting of self-driving cars, drones, boosted truth, facial recognition, and a lot, much more . Incredible new computer system vision applications are created each day, thanks to fast developments in AI as well as deep learning (DL) . Deep Learning for Vision...
Packt Publishing, 2018. — 496 p. — ISBN: 978-1-78899-745-4. Java Deep Learning Projects: Implement 10 real-world deep learning applications using Deeplearning4j and open-source APIs Build and deploy powerful neural network models using the latest Java deep learning libraries Java is one of the most widely used programming languages. With the rise of deep learning, it has become...
Packt Publishing, 2018. — 428 p. — ISBN: 978-1-78899-745-4. Java Deep Learning Projects: Implement 10 real-world deep learning applications using Deeplearning4j and open-source APIs Build and deploy powerful neural network models using the latest Java deep learning libraries Java is one of the most widely used programming languages. With the rise of deep learning, it has become...
2nd edition. — Apress, 2021. — 316 p. — ISBN: 978-1484253632. Code files only! Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real-world problems with a sound theoretical foundation and practical know-how...
Apress, 2017. — 169 p. — ISBN-13 (electronic): 978-1-4842-2766-4. Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of...
Apress, 2017. — 169 p. — ISBN-13 (electronic): 978-1-4842-2766-4. Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of...
CRC Press, 2023. — 414 p. — (The R Series). — ISBN: 978-1-032-23139-6. This is a book about torch, the R interface to PyTorch. PyTorch, as of this writing, is one of the major Deep Learning and scientific-computing frameworks, widely used across industries and areas of research. With Torch, you get to access its rich functionality directly from R, with no need to install, let...
Elsevier, 2024. — 334 p. — ISBN: 978-0-443-21432-5. Applications of Deep Machine Learning in Future Energy Systems push the limits of current Artificial Intelligence techniques to present deep machine learning suitable for the complexity of sustainable energy systems. The first two chapters take the reader through the latest trends in power engineering and system design and...
No Starch Press, 2021. — 464 p. — ISBN13: 978-1-7185-0075-4 (ebook). Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your DL projects. If you’ve been curious about machine learning but didn’t know where to start, this is the book you’ve been waiting for. Focusing on the subfield of machine learning known...
Addison-Wesley Professional, 2019. — 871 p. — (Addison-Wesley Data & Analytics Series). Deep learning is one of today's hottest fields. In Deep Learning Illustrated , three world-class instructors and practitioners present a uniquely visual, intuitive, and accessible high-level introduction to the techniques and applications of deep learning. Packed with vibrant, full-color...
CRC Press, 2023. — 246 p. — ISBN: 978-1-003-34868-9. Deep Learning Approach for Natural Language Processing, Speech, and Computer Vision provides an overview of general deep learning methodology and its applications of Natural Language Processing (NLP), speech, and Computer Vision tasks. It simplifies and presents the concepts of Deep Learning comprehensively, with suitable,...
CRC Press, 2023. — 246 p. — ISBN: 978-1-032-39166-3. The deep Learning Approach for Natural Language Processing, Speech, and Computer Vision provides an overview of general deep learning methodology and its applications of Natural Language Processing (NLP), speech, and Computer Vision tasks. It simplifies and presents the concepts of Deep Learning comprehensively, with...
Manning Publishing, 2019. — 240 p. — ISBN: 978-1617295560. GANs in Action teaches you how to build and train your Generative Adversarial Networks, one of the most important innovations in deep learning. In this book, you’ll learn how to start building your simple adversarial system as you explore the foundation of GAN architecture: the generator and discriminator networks....
LazyProgrammer, 2016. — 46 p. When we talk about modern deep learning, we are often not talking about vanilla neural networks - but newer developments, like using Autoencoders and Restricted Boltzmann Machines to do unsupervised pre-training. Deep neural networks suffer from the vanishing gradient problem, and for many years researchers couldn’t get around it - that is, until...
World Scientific Publishing, 2024. — 493 p. — ISBN: 978-981-12-8649-0. 3D Deep Learning is a rapidly evolving field that has the potential to transform various industries. This book provides a comprehensive overview of the current state-of-the-art in 3D Deep Learning, covering a wide range of research topics and applications. It collates the most recent research advances in 3D...
Springer, 2020. — 160 p. — ISBN: 978-981-13-2386-7. This book introduces the applications of deep learning in various human centric visual analysis tasks, including classical ones like face detection and alignment and some newly rising tasks like fashion clothing parsing. Starting from an overview of current research in human centric visual analysis, the book then presents a...
Springer Nature, 2019. — 452 p. — ISBN: 978-3-319-42999-1. This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly...
Editora Dialetica, 2022. — 121 p. Recently, Deep Learning has caused a significant impact on computer vision, speech recognition, and natural language understanding. Despite the remarkable advances, Deep Learning (DL) recent performance gains have been modest and usually rely on increasing the depth of the models, which often requires more computational resources such as...
Artech House, 2020. — 313 p. — ISBN 978-1-63081--637-7. This authoritative resource presents a comprehensive illustration of modern Artificial Intelligence / Machine Learning (AI/ML) technology for radio frequency (RF) data exploitation. It identifies technical challenges, benefits, and directions of deep learning (DL) based object classification using radar data, including...
Hoboken: Wiley, 2024. — 405 p. An engaging and accessible introduction to Deep Learning perfect for students and professionals. In Deep Learning: A Practical Introduction, a team of distinguished researchers delivers a book complete with coverage of the theoretical and practical elements of Deep Learning. The book includes extensive examples, end-of-chapter exercises, homework,...
Apress, 2018. — 184 p. — ISBN: 148423720X. Discover the essential building blocks of a common and powerful form of deep belief network: convolutional nets. This book shows you how the structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a ‘thought process’ that is capable of learning abstract concepts built from...
Apress, 2018. — 200 p. — ISBN-13 (electronic): 978-1-4842-3591-1. Discover the essential building blocks of the most common forms of deep belief networks. At each step this book provides intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive...
Apress, 2018. — 262 p. — ISBN-13 (electronic): 978-1-4842-3646-8. Discover the essential building blocks of a common and powerful form of deep belief net: the autoencoder. You’ll take this topic beyond current usage by extending it to the complex domain for signal and image processing applications. Deep Belief Nets in C++ and CUDA C: Volume 2 also covers several algorithms for...
Apress, 2018. — 262 p. — ISBN-13 (electronic): 978-1-4842-3646-8. Discover the essential building blocks of a common and powerful form of deep belief net: the autoencoder. You’ll take this topic beyond current usage by extending it to the complex domain for signal and image processing applications. Deep Belief Nets in C++ and CUDA C: Volume 2 also covers several algorithms for...
Apress, 2018. — 165 p. — ISBN-13 (electronic): 978-1-4842-3721-2. Discover the essential building blocks of a common and powerful form of deep belief network: convolutional nets. This book shows you how the structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a ‘thought process’ that is capable of learning abstract...
Anita Gehlot, Dolly Sharma, Monika Mangla, Rajesh Singh, Sergio Márquez Sánchez, Vaishali Mehta. — Bentham Science Publishers, 2022. — 228 p. — ISBN: 978-981-5036-08-4. The competence of deep learning in the automation and manufacturing sector has received astonishing attention in recent times. The manufacturing industry has recently experienced a revolutionary advancement...
Apress, 2018. — 425 p. — ISBN13: 978-1-4842-3790-8. Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You'll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and...
Morgan & Claypool, 2020. — 199 p. — ISBN: 9781681737607. Text production has many applications. It is used, for instance, to generate dialogue turns from dialogue moves, verbalise the content of knowledge bases, or generate English sentences from rich linguistic representations, such as dependency trees or abstract meaning representations. Text production is also at work in...
Springer, 2020. — 768 p. — (Springer Series in the Data Sciences). — ISBN: 9783030367206, 9783030367213. This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks,...
Apress, 2021. — 388 p. — ISBN: 978-1-4842-7340-1. Use TensorFlow 2.x in the Google Colab ecosystem to create state-of-the-art deep learning models guided by hands-on examples. The Colab ecosystem provides a free cloud service with easy access to on-demand GPU (and TPU) hardware acceleration for fast execution of the models you learn to build. This book teaches you,...
BPB Publications, 2021 — 440 p. — ISBN: 9789389898118. Learn how to redesign NLP applications from scratch. Natural language processing (NLP) is one of the areas where many Machine Learning and Deep Learning techniques are applied. This book covers wide areas, including the fundamentals of Machine Learning, Understanding and optimizing Hyperparameters, Convolution Neural...
2nd Edition. — Apress Media, LLC., 2023. — 672 p. — ISBN13: 978-1-4842-8931-0. This book builds upon the foundations established in its first edition, with updated chapters and the latest code implementations to bring it up-to-date with Tensorflow 2.0. Pro Deep Learning with TensorFlow 2.0 begins with the mathematical and core technical foundations of deep learning. Next, you...
BISAC: Computers / Intelligence (AI) & Semantics, 2018. — 394 p. I challenge you to find a field as interesting and exciting as Deep Learning. This book is a spin-off from my previous book "The Deep Learning AI Playbook". The Playbook was meant for a professional audience. This is targeted to a much wider audience. There are two kinds of audiences, those looking to explore and...
Pragmatic Programmers, LLC., 2020. — 342 p. — ISBN: 978-1680506600. You’ve decided to tackle machine learning – because you’re job hunting, embarking on a new project, or just think self-driving cars are cool. But where to start? It’s easy to be intimidated, even as a software developer. The good news is that it doesn’t have to be that hard. Master machine learning by writing...
CRC Press, 2024. — 244 p. — eBook ISBN: 978-1-003-29612-6. Natural Language Processing (NLP) is a sub-field of Artificial Intelligence, linguistics, and computer science and is concerned with the generation, recognition, and understanding of human languages, both written and spoken. NLP systems examine the grammatical structure of sentences as well as the specific meanings of...
Manning Publications, 2019. — 384 p. — ISBN: 9781617295324. Deep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. As you progress, you’ll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You’ll enjoy watching your bot master the game of Go, and along the way, you’ll...
Manning Publications, 2022. — 602 p. — ISBN: 13: 978-1617298639. Written for everyday developers, there are no complex mathematical proofs or unnecessary academic theories in Inside Deep Learning. Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems. Inside Deep Learning is a fast-paced beginner's guide...
Manning, 2019. - 266 p. - ISBN: 9781617295447 Humans do a great job of reading text, identifying key ideas, summarizing, making connections, and other tasks that require comprehension and context. Recent advances in deep learning make it possible for computer systems to achieve similar results. Deep Learning for Natural Language Processing teaches you to apply deep learning...
BPB Publications, 2025. — 476 p. — ISBN-13: 978-93-65890-846. Description Explore the world of generative AI, a technology capable of creating new data that closely resembles reality. This book covers the fundamentals and advances through cutting-edge techniques. It also clarifies complex concepts, guiding you through the essentials of deep learning, neural networks, and the...
O’Reilly, 2019. — 253 p. — ISBN13: 978-1-492-03983-9 Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields....
Packt Publishing Ltd, 2020. — 332 p. Build autonomous vehicles using deep neural networks and behavior-cloning techniques With self-driving cars (SDCs) being an emerging subject in the field of artificial intelligence, data scientists have now focused their interest on building autonomous cars. This book is a comprehensive guide to using deep learning and computer vision...
Wiley-Scrivener, 2024. — 280 p. Dive into this 15-chapter book on ‘Deep Learning Techniques’ and how its solutions allow computers to learn from experience and understand hierarchy concepts. It provides approaches to deep learning in areas of detection, prediction, and future framework development.
CRC Press, 2023. — 223 p. This book explores how to use generative adversarial networks in a variety of applications and emphasizes their substantial advancements over traditional generative models. It concentrates on cutting-edge research in DL and GAN which includes creating new tools and methods for processing text, images, and audio.
MDPI, 2023. — 284 p. This book highlights the importance of Deep Learning (DL), which has garnered significant attention in science, industry, and academia. It draws inspiration from the functioning of the human brain and the concept of learning. Unlike traditional and Machine Learning (ML) methods, Deep Learning techniques emulate the human brain's neural networks at a lower...
Manning Publications Co., 2025. — 504 p. — ISBN: 978-1633438545. Business runs on tabular data in databases, spreadsheets, and logs. Crunch that data using deep learning, gradient boosting, and other machine learning techniques. Machine Learning for Tabular Data teaches you to train insightful machine learning models on common tabular business data sources such as spreadsheets,...
Artech House, 2021. — 361 p. — ISBN: 978-1-63081-746-6. This exciting new resource presents emerging applications of artificial intelligence and deep learning in short-range radar. The book covers applications ranging from industrial, consumer space to emerging automotive applications. The book presents several human-machine interface (HMI) applications, such as gesture...
Santra Avik, Hazra Souvik, Servadei Lorenzo, Thomas Stadelmayer, Michael Stephan, Anand Dubey. — Wiley-IEEE Press, 2023. — 332 p. — ISBN: 978-1119910657. Introduces multiple state-of-the-art Deep Learning architectures for mmwave radar in a variety of advanced applications. Methods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions provide a timely and...
Manning Publications, 2024. — 408 p. — ISBN-13: 978-1633438880. Accelerate deep learning and other number-intensive tasks with JAX, Google’s awesome high-performance numerical computing library. The JAX numerical computing library tackles the core performance challenges at the heart of deep learning and other scientific computing tasks. By combining Google’s Accelerated Linear...
BPB Publications, 2022. — 315 p. This book begins with the configuration of an Anaconda development environment, essential for practicing the deep learning process. The basics of machine learning, which are needed for Deep Learning, are explained in this book. TensorFlow is the industry-standard library for Deep Learning, and thereby, it is covered extensively with both...
Springer, 2020. — 144 p. — ISBN: 978-3-030-37590-4. This stimulating text/reference presents a philosophical exploration of the conceptual foundations of deep learning, presenting enlightening perspectives that encompass such diverse disciplines as computer science, mathematics, logic, psychology, and cognitive science. The text also highlights select topics from the...
Independently published, 2020. — 182 p.— ISBN B08S1C9KST. Are you interested in taking your deep learning knowledge to the next level? Then this is the book for you! Machine and deep learning are the future, and there’s no getting away from that. So learning it now, and learning how to do it the right way will put you ahead of the crowd. Deep learning is all about understanding...
Independently published, 2022. — 297 p. Learn how to fine-tune the current state-of-the-art EffecientNet V2 model to perform image classification on satellite data (EuroSAT) using TensorFlow in Python. Satellite image classification is undoubtedly crucial for many applications in agriculture, environmental monitoring, urban planning, and more. Applications such as crop...
Academic Press, 2021. — 427 p. — ISBN: 978-0-12-823519-5. Generative Adversarial Networks (GAN) have started a revolution in Deep Learning, and today GAN is one of the most researched topics in Artificial Intelligence. Generative Adversarial Networks for Image-to-Image Translation provides a comprehensive overview of the GAN (Generative Adversarial Network) concept starting...
Springer, 2023. — 483 p. This book is a comprehensive curation, exposition, and illustrative discussion of recent research tools for the interpretability of Deep Learning models, with a focus on neural network architectures. In addition, it includes several case studies from application-oriented articles in the fields of Computer Vision, optics, and Machine Learning related...
BPB Publications, 2023. — 624 p. — ISBN: 978-93-55513-724. A comprehensive guide to Deep Learning for Beginners. Key Features Learn how to design your neural network efficiently. Learn how to build and train Recurrent Neural Networks (RNNs). Understand how encoding and decoding work in Deep Neural Networks. Description Deep Learning has become increasingly important due to the...
Manning Publications, 2023. — 623 p. — ISBN: 978-1633439023. Guide machine learning projects from design to production with the techniques in this unique project management guide. No ML skills are required! In Managing Machine Learning Projects you’ll learn essential machine learning project management techniques, including: Understanding an ML project’s requirements. Setting...
Bentham Books, 2023. — 270 p. — ISBN: 978-981-5079-23-4. This book is a detailed reference guide on Deep Learning and its applications. It aims to provide a basic understanding of Deep Learning and its different architectures that are applied to process images, speech, and natural language. It explains basic concepts and many modern use cases through fifteen chapters...
Springer, 2021. — 314 p. — ISBN: 9811567581. This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019). It focuses on deep learning networks and their application in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial...
Springer, 2023. — 408 p. This book is intended for students, engineers, and researchers interested in both computational mechanics and Deep Learning. It presents the mathematical and computational foundations of Deep Learning (DL) with detailed mathematical formulas in an easy-to-understand manner. It also discusses various applications of Deep Learning in Computational...
ITexLi, 2023. — 110 p. — ISBN: 1803569514 9781803569512 1803569506 9781803569505 1803569522 9781803569529. Deep learning and reinforcement learning are some of the most important and exciting research fields today. With the emergence of new network structures and algorithms such as convolutional neural networks, recurrent neural networks, and self-attention models, these...
Cambridge: Cambridge University Press, 2021. — 339 p. Deep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established methods from the basic to...
Apress Media LLC, 2022. — 463 p. — ISBN13: 978-1-4842-7412-5. Learn how to harness modern deep-learning methods in many contexts. Packed with intuitive theory, practical implementation methods, and deep-learning case studies, this book reveals how to acquire the tools you need to design and implement like a deep-learning architect. It covers tools deep learning engineers can...
Independently published, 2021. — 1029 p. Release 0.16.6, Jun 25, 2021 This book represents our attempt to make deep learning approachable, teaching you the concepts, the context, and the code. Just a few years ago, there were no legions of deep learning scientists developing intelligent products and services at major companies and startups. When we entered the field, machine...
Springer, 2023. — 287 p. This book is very beneficial for early researchers/faculty who want to work in Deep Learning and Machine Learning for the classification domain. It helps them study, formulate, and design their research goal by aligning the latest technologies and studies’ image and data classifications. The early start-up can use it to work with product or prototype...
ITexLi, 2020. — 103 p. — ISBN: 1839628790 9781839628795 1839628804 9781839628801. This book examines Deep Learning (DL) applications and future trends in the field. It is a useful resource for researchers and students alike. Artificial Intelligence (AI) has attracted the attention of researchers and users alike and is taking an increasingly crucial role in our modern society....
Springer, 2021 p. - 274 p. - ISBN: 9783030656607 This book contributes to the progress towards intelligent transportation. It emphasizes new data management and machine learning approaches such as big data, deep learning and reinforcement learning. Deep learning and big data are very energetic and vital research topics of today’s technology. Road sensors, UAVs, GPS, CCTV and...
Springer Cham, 2023. — 94 p. — (Synthesis Lectures on Engineering, Science, and Technology) — eBook ISBN: 978-3-031-38133-1. Explores different design aspects associated with each number system and their effects on DNN performance. Discusses the most efficient number systems for DNNs hardware realization. Describes various number systems and their usage for Deep Neural Network...
Apress, 2020. — 356 p. — ISBN: 9781484264300. Build deep learning and computer vision systems using Python, TensorFlow, Keras, OpenCV, and more, right within the familiar environment of Microsoft Windows. The book starts with an introduction to tools for deep learning and computer vision tasks followed by instructions to install, configure, and troubleshoot them. Here, you will...
Morgan & Claypool Publishers , 2020. - 111 p. - ISBN: 9781681739137 This book provides a short introduction and easy-to-follow implementation steps of deep learning using Google Cloud Platform. It also includes a practical case study that highlights the utilization of Python and related libraries for running a pre-trained deep learning model. In recent years, deep...
John Wiley & Sons, Inc., 2025. — 256 p. — ISBN: 978-1-394-26927-3. A comprehensive, accessible introduction to deep learning for engineering tasks through Python programming, low-cost hardware, and freely available software. Deep Learning on Embedded Systems is a comprehensive guide to the practical implementation of deep learning for engineering tasks through computers and...
2nd ed. — Birmingham: Packt Publishing, 2020. — 512 p. — ISBN: 1838821651. Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and Keras ! Key Features Explore the most advanced deep learning techniques that drive modern AI results. New coverage of unsupervised deep learning using mutual information, object detection, and...
John Wiley & Sons, Inc., 2023. — 256 p. — eBook ISBN: 978-1-394-16777-7. This book thoroughly explains deep learning models and how to use Python programming to implement them in applications such as NLP, face detection, face recognition, face analysis, and virtual assistance (chatbot, machine translation, etc.). It provides hands-on guidance in using Python for implementing...
Birmingham: Packt Publishing, 2023. — 385 p. — ISBN: 180324688X. Develop Bayesian Deep Learning models to help make your applications more robust. Key Features Learn how advanced convolutions work. Learn to implement a convolution neural network. Learn advanced architectures using convolution neural networks. Apply Bayesian NN to decrease weighted distribution. Bayesian Deep...
IOP Publishing, 2022. — 267 p. — (IOP Series in Next Generation Computing). Artificial Intelligence (AI) is gaining traction in areas of social responsibility. From climate change to social polarization to epidemics, humankind has been seeking new solutions to these ever-present problems. Deep Learning (DL) techniques have increased in power in recent years, with algorithms...
Siddhartha Bhattacharyya, Vaclav Snasel, Aboul Ella Hassanien, Satadal Saha, B. K. Tripathy (Eds.). — Walter de Gruyter, 2020. — 179 p. — ISBN: 978-3110670790. This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative...
Packt Publishing, 2019. — 372 p. — ISBN: 978-1-83855-029-5. Explore deep neural network architectures and their application areas to overcome your NLP issues Key Features Get to grips with the basic building blocks of natural language processing Understand how to select the most suitable deep neural network to solve your NLP problems Explore convolutional and recurrent neural...
Manning, 2020. — 350 p. — ISBN: 9781617296178. Deep learning has transformed the fields of computer vision, image processing, and natural language applications. Thanks to TensorFlow.js, now JavaScript developers can build deep learning apps without relying on Python or R. Deep Learning with JavaScript shows developers how they can bring DL technology to the web. Written by the...
Wiley, 2021. — 435 p. — ISBN: 9781119646143. Deep Learning for the Earth Sciences: Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link...
Anupam Ghosh, Jyotsna Kumar Mandal, Rajdeep Chakraborty, S. Balamurugan. — Wiley-Scrivener, 2023. — 480 p. — (Artificial Intelligence and Soft Computing for Industrial Transformation). — ISBN13: 978-1119857211. In-depth analysis of Deep Learning-based cyber-IoT systems and security which will be the industry leader for the next ten years. Deep Learning (also known as deep...
Academic Press, 2021. — 230 p. — ISBN: 978-0-323-90184-0. Deep Learning for Chest Radiographs enumerates different strategies implemented by the authors for designing an efficient convolution neural network-based computer-aided classification (CAC) system for binary classification of chest radiographs into "Normal" and "Pneumonia." Pneumonia is an infectious disease mostly...
CRC Press, 2022. — 177 p. — (Explainable AI (XAI) for Engineering Applications). Over the last decade, progress in deep learning has had a profound and transformational effect on many complex problems, including speech recognition, machine translation, natural language understanding, and computer vision. As a result, computers can now achieve human-competitive performance in a...
Springer, 2021. — 403 p. — ISBN: 978-981-16-2232-8. This book systematically introduces readers to the theory of deep learning and explores its practical applications based on the MindSpore AI computing framework. Divided into 14 chapters, the book covers deep learning, deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs),...
World Scientific Publishing Co. Pte. Ltd., 2021. — 327 p. — ISBN: 9811218838. Deep Learning has achieved great success in many challenging research areas, such as image recognition and natural language processing. The key merit of deep learning is to automatically learn good feature representation from massive data conceptually. In this book, we will show that the deep learning...
Nova, 2020. - 338 p. - ISBN: 9789813348844 From the successful application of deep learning (DL) in AlphaGo in 2012 to the recent advances in edge computing, artificial intelligence (AI) has continued to develop over the years. In the face of the current sweeping trend of AI, ensemble learning (EL) is expected to be further applied to DL and AI for developing higher-level...
Springer, 2023. — 314 p. A critical challenge in Deep Learning is the vulnerability of Deep Learning networks to security attacks from intelligent cyber adversaries. Even innocuous perturbations to the training data can be used to manipulate the behavior of deep networks in unintended ways. In this book, we review the latest developments in adversarial attack technologies in...
Chivukula Aneesh Sreevallabh, Yang Xinghao, Liu Bo, Liu Wei, Zhou Wanlei. — Springer, 2023. — 319 p. — ISBN: 978-3-030-99772-4. A critical challenge in Deep Learning is the vulnerability of Deep Learning networks to security attacks from intelligent cyber adversaries. Even innocuous perturbations to the training data can be used to manipulate the behavior of deep networks in...
Manning Publications, 2018. — 392 p. — ISBN13: 978-1617295546. Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples. Machine learning has made remarkable progress in recent years. Deep-learning systems now...
Manning, 2018. — 325 p. Artificial intelligence has made some incredible leaps. Deep learning systems now deliver near-human speech and image recognition, not to mention machines capable of beating world champion Go masters. Deep learning applies to a widening range of problems, such as question answering, machine translation, and optical character recognition. It's behind...
New Delhi: BPB Publications, 2019. — 357 p. Deep Learning with Kelp.Net is the ultimate reference for C# .Net developers who are passionate about deep learning. Readers will learn all the skills necessary to develop powerful, scalable and flexible deep learning models from a fluid and easy to use API. Upon completing the book the reader will have all the tools necessary to add...
LazyProgrammer, 2016. — 59 p. This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural...
The Institution of Engineering and Technology, 2020. — 328 p. — ISBN: 978-1-78561-769-0. This book presents and discusses innovative ideas in the design, modeling, implementation, and optimization of hardware platforms for neural networks. The rapid growth of server, desktop, and embedded applications based on deep learning has brought about a renaissance in interest in neural...
Amazon Digital Services LLC, 2018. — 98 p. — ASIN: B07K2Q6DXH. Deep learning is a process that widens the range of most artificial intelligence problems like speech recognition, image classification, question answering, optical character recognition, and transforming text to speech. It is true that deep learning is a complex subject to learn and understand, but it is not...
Packt, 2020. — 364 p. — ASIN B085P1JG2W. A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures Key Features Understand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networks Learn the mathematical concepts needed to understand how deep learning models...
LazyProgrammer, 2016. — 71 p. Deep learning is making waves. At the time of this writing (March 2016), Google’s AlghaGo program just beat 9-dan professional Go player Lee Sedol at the game of Go, a Chinese board game. Experts in the field of Artificial Intelligence thought we were 10 years away from achieving a victory against a top professional Go player, but progress seems to...
Packt Publishing, 2018. — 420 p. — ISBN: 978-1-78588-036-0. Get to grips with the essentials of deep learning by leveraging the power of Python Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training...
Packt Publishing, 2018. — 420 p. — ISBN: 978-1-78588-036-0. Get to grips with the essentials of deep learning by leveraging the power of Python Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training...
PB Publications, 2023. — 237 p. — ISBN: 978-93-5551-105-8. A step-by-step guide to getting started with Machine Learning. Key Features Understand different types of Machine Learning like Supervised, Unsupervised, Semi-supervised, and Reinforcement learning. Learn how to implement Machine Learning algorithms effectively and efficiently. Get familiar with the various libraries &...
Addison-Wesley, 2021. — 800 p. — ISBN: 9780137470198. NVIDIA's Full-Color Guide to Deep Learning with TensorFlow: All You Need to Get Started and Get Results. Deep learning is a key component of today's exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to deep learning with TensorFlow, the #1 Python library for...
Springer Cham, 2022. — 341 p. — (Computational Intelligence Methods and Applications). — ISBN: 978-3-031-04420-5. This book focuses on the applied aspects of artificial intelligence using enterprise frameworks and technologies. The book is applied in nature and will equip the reader with the necessary skills and understanding for delivering enterprise ML technologies. It will...
Boca Raton: CRC Press, 2023. — 228 p. The leveraging of Artificial Intelligence (AI) for model discovery in dynamical systems are cross-fertilizing and revolutionizing both disciplines, heralding a new era of data-driven science. This book is placed at the forefront of this endeavor, taking model discovery to the next level. Dealing with Artificial Intelligence, this book...
CRC Press, 2023. — 228 p. — (Artificial Intelligence and Robotics Series). — ISBN: 978-1-032-36632-6. The leveraging of Artificial Intelligence (AI) for model discovery in dynamic systems is cross-fertilizing and revolutionizing both disciplines, heralding a new era of data-driven science. This book is placed at the forefront of this endeavor, taking model discovery to the next...
Applied Data Science Partners Ltd, 2019. — 308 p. — ISBN: 978-1-492-04194-8. Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive...
Applied Data Science Partners Ltd, 2019. — 308 p. — ISBN: 978-1-492-04194-8. Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive...
2nd Edition. —Manning Publications, 2021. — 504 p. — ISBN: 978-1617296864. Unlock the groundbreaking advances of deep learning with this extensively revised new edition of the bestselling original. Learn directly from the creator of Keras and master practical Python deep learning techniques that are easy to apply in the real world. In Deep Learning with Python, Second Edition...
Academic Press, 2020. — 300 p. — ISBN: 0323909337. Introduction to Deep Learning and Neural Networks with Python: A Practical Guide is an intensive step-by-step guide for neuroscientists to fully understand, practice, and build neural networks. Providing math and Python code examples to clarify neural network calculations, by book’s end readers will fully understand how neural...
CRC Press, 2022. — 267 p. — ISBN: 978-1-032-06470-3. This book promotes and facilitates exchanges of research knowledge and findings across different disciplines on the design and investigation of deep learning (DL)–based data analytics of IoT (Internet of Things) infrastructures. Deep Learning for Internet of Things Infrastructure addresses emerging trends and issues on IoT...
Online version, The MIT Press, 2016. 716 p. This book has been organized into three parts to best accommodate a variety of readers. Part I introduces basic mathematical tools and machine learning concepts. Part II describes the most established deep learning algorithms, which are essentially solved technologies. Part III describes more speculative ideas that are widely believed...
Independently published, 2020. — 159 p. — ISBN B08NCGCC5M. Are you interested in Machine Learning? Are you fascinated by how robots work? Are you ready to open up to the dynamics of technological change? Machine Learning has been approached in a definitive manner as a subset falling under a larger set of Artificial intelligence . It majorly focuses on the aspect of learning of...
Apress Media LLC, 2022. — 395 p. — ISBN13: 978-1-4842-8148-2. Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learning approaches and provides techniques to facilitate your deep learning model development. The first...
Apress Media LLC, 2022. — 395 p. — ISBN13: 978-1-4842-8148-2. Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learning approaches and provides techniques to facilitate your deep learning model development. The first...
New York: Engineering Science Reference, 2019. — 374 p. Leading technology firms and research institutions are continuously exploring new techniques in artificial intelligence and machine learning. As such, deep learning has now been recognized in various real-world applications such as computer vision, image processing, biometrics, pattern recognition, and medical imaging. The...
Springer, 2022. — 117 p. — (Synthesis Lectures on Computer Vision). — ISBN: 978-3-031-14594-0. This book presents and surveys normalization techniques with deep analysis of training deep neural networks. In addition, the author provides technical details in designing new normalization methods and network architectures tailored to specific tasks. Normalization methods can...
Academic Press, 2021. — 353 p. — ISBN: 978-0-323-90198-7. Principles and Labs for Deep Learning provides the knowledge and techniques needed to help readers design and develop deep learning models. Deep Learning techniques are introduced through theory, comprehensively illustrated, explained through the TensorFlow source code examples, and analyzed through the visualization of...
River Publishers, 2022. — 312 p. Health care today is known to suffer from siloed and fragmented data delayed clinical communications and disparate workflow tools due to the lack of interoperability caused by vendor-locked healthcare systems, lack of trust among data holders, and security/privacy concerns regarding data sharing. The health information industry is ready for big...
Springer Nature Singapore Pte Ltd., 2019. — 237 p. — ISBN: 978-981-10-5152-4 (eBook). This book discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and...
Springer, 2023. — 433 p. — e-ISBN: 978-3-031-32879-4. This book provides a conceptual understanding of Deep Learning algorithms. The book consists of four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation...
3rd ed. — Birmingham: Packt Publishing, 2022. — 698 p. — ISBN: 1803232919. Build cutting-edge machine and deep learning systems for the lab, production, and mobile devices. Key Features Understand the fundamentals of deep learning and machine learning through clear explanations and extensive code samples. Implement graph neural networks, transformers using Hugging Face and...
Packt Publishing, 2018. — 496 p. — ISBN: 978-1-78899-745-4. Java Deep Learning Projects: Implement 10 real-world deep learning applications using Deeplearning4j and open-source APIs Build and deploy powerful neural network models using the latest Java deep learning libraries Java is one of the most widely used programming languages. With the rise of deep learning, it has become...
BPB Publications, 2020. — 205 p. — ISBN: 978-93-89328-684. Learn modern-day technologies from modern-day technical giants. DESCRIPTION. This book aims to help readers understand the concept of artificial intelligence and deep learning methods and implement them in their businesses and organizations. The first two chapters describe the introduction of artificial intelligence and...
MIT, 2019. - 297 p. Deep learning is enabling innovation and change across all aspects of our modern lives. Most of the artificial intelligence breakthroughs that you hear about in the media are based on deep learning. As a result, whether you are a business person interested in improving the efficiency of your organization, a policymaker concerned with ethics and privacy in a...
The MIT Press, 2019. — 196 p. — (MIT Press Essential Knowledge series). — ISBN: 978-0262537551. An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones,...
The MIT Press, 2019. — 196 p. — (MIT Press Essential Knowledge series). — ISBN: 978-0262537551. An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones,...
Leanpub, 2022. — 163 p. Zefs Guide to Deep Learning is a short guide to the most important concepts in Deep Learning, the technique at the center of the current Artificial Intelligence (AI) revolution. It will give you a strong understanding of the core ideas and most important methods and applications. All in around only 150 p.! This book presents the foundational concepts...
Leanpub, 2023-05-31. — 163 p. (2023-05-31 Update) Zefs Guide to Deep Learning is a short guide to the most important concepts in Deep Learning, the technique at the center of the current Artificial Intelligence (AI) revolution. It will give you a strong understanding of the core ideas and most important methods and applications. All in around 150 p.! This book presents the...
Singapore: Springer Singapor, 2018. — 79 p. — ISBN: 978-981-13-1444-5. This book presents recent advances in intrusion detection systems (IDSs) using state-of-the-art deep learning methods. It also provides a systematic overview of classical machine learning and the latest developments in deep learning. In particular, it discusses deep learning applications in IDSs in different...
3rd Edition. — De Gruyter, 2023. — 574 p. — (De Gruyter Textbook). — ISBN: 978-3111028119. Reading the book, you can feel the long practical experience of the author. The text is easy to read, even where concepts can be complex. The strong theoretical background of the author is well-known from other publications. In this book, however, the topics are presented on a level that...
No Starch Press, 2022. — 344 p. — ISBN: 978-1-7185-0190-4. With Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning. You'll work through Python examples to learn key deep learning-related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in...
No Starch Press, 2022. — 344 p. — ISBN: 978-1-7185-0190-4. With Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning. You'll work through Python examples to learn key deep learning related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in...
Boca Raton: CRC Press, 2021. — 365 p. This book is a detailed reference on biomedical applications using Deep Learning. Because Deep Learning is an important actor shaping the future of Artificial Intelligence, its specific and innovative solutions for both medical and biomedical are very critical. This book provides a recent view of research works on essential, and?advanced...
Springer, 2021. — 732 p. — ISBN: 978-3-030-77938-2. This book is used at the graduate or advanced undergraduate level and many others. Manned and unmanned ground, aerial and marine vehicles enable many promising and revolutionary civilian and military applications that will change our lives shortly. These applications include, but are not limited to, surveillance, search and...
Morgan & Claypool Publishers , 2020. - 164 p. - ISBN: 1681738694 This Synthesis Lecture focuses on techniques for efficient data orchestration within DNN accelerators. The End of Moore's Law, coupled with the increasing growth in deep learning and other AI applications has led to the emergence of custom Deep Neural Network (DNN) accelerators for energy-efficient inference on...
CRC Press 2023. — 216 p. — eBook ISBN: 978-1-003-45632-2. This book covers the description of both conventional methods and advanced methods. In conventional methods, visual tracking techniques such as stochastic, deterministic, generative, and discriminative are discussed. The conventional techniques are further explored for multi-stage and collaborative frameworks. In...
CRC Press; Taylor & Francis Group, 2021. — 162 p. — (Chapman & Hall/CRC Machine Learning & Pattern Recognition). — ISBN: 978-0367-64947-0. The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For some of these applications, deep learning models now...
LazyProgrammer, 2016. — 46 p. When we talk about modern deep learning, we are often not talking about vanilla neural networks - but newer developments, like using Autoencoders and Restricted Boltzmann Machines to do unsupervised pre-training. Deep neural networks suffer from the vanishing gradient problem, and for many years researchers couldn’t get around it - that is, until...
CRC, 2021. — 338 p. — ISBN: 9781536189896. Using the implementation of a deep learning framework as an example, C++ Template Metaprogramming in Practice: A Deep Learning Framework explains the application of metaprogramming in a relatively large project and emphasizes ways to optimize systems performance. The book is suitable for developers with a basic knowledge of C++....
Birmingham: Packt Publishing, 2022. — 287 p. — ISBN: 1803241330. Train, test, run, track, store, tune, deploy, and explain provenance-aware deep learning models and pipelines at scale with reproducibility using MLflow. Key Features Focus on deep learning models and MLflow to develop practical business AI solutions at scale. Ship deep learning pipelines from experimentation to...
Springer Nature, 2019. — 452 p. — ISBN: 978-3-030-13969-8 (eBook). This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It...
Springer Nature, 2019. — 452 p. — ISBN: 978-3-030-13969-8 (eBook). This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It...
Springer Nature, 2019. — 452 p. — ISBN: 978-3-030-13969-8 (eBook). This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It...
O’Reilly Media, Inc., 2024. — 458 p. — ISBN: 978-1-098-14528-6. Bringing a deep-learning project into production at scale is quite challenging. To successfully scale your project, a foundational understanding of full-stack deep learning, including the knowledge that lies at the intersection of hardware, software, data, and algorithms, is required. This book illustrates complex...
Apress, 2018. — 200 p. — ISBN-13 (electronic): 978-1-4842-3591-1. Discover the essential building blocks of the most common forms of deep belief networks. At each step this book provides intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive...
Apress, 2018. — 262 p. — ISBN-13 (electronic): 978-1-4842-3646-8. Discover the essential building blocks of a common and powerful form of deep belief net: the autoencoder. You’ll take this topic beyond current usage by extending it to the complex domain for signal and image processing applications. Deep Belief Nets in C++ and CUDA C: Volume 2 also covers several algorithms for...
Apress, 2018. — 165 p. — ISBN-13 (electronic): 978-1-4842-3721-2. Discover the essential building blocks of a common and powerful form of deep belief network: convolutional nets. This book shows you how the structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a ‘thought process’ that is capable of learning abstract...
ITexLi, 2021. — 192 p. — ISBN: 9781839623752. This volume is dedicated to deep learning - a branch of machine learning similar to artificial intelligence. The applications of deep learning vary from medical imaging to industrial quality checking, sports, and precision agriculture. The book is divided into two sections. The first section covers deep learning architectures and...
CRC Press, 2024. — 199 p. — ISBN: 9781032487960. This book focuses on Deep Learning (DL), which is an important aspect of Data Science, that includes predictive modeling. DL applications are widely used in domains such as finance, transport, healthcare, auto manufacturing, and advertising. The design of the DL models based on artificial neural networks is influenced by the...
Gistrup: River Publishers, 2023. — 286 p. Object detection is a basic visual identification problem in computer vision that has been explored extensively over the years. Visual object detection seeks to discover objects of specific target classes in a given image with pinpoint accuracy and apply a class label to each object instance. Object recognition strategies based on deep...
Springer, 2022. — 171 p. — (T-Labs Series in Telecommunication Services). — ISBN13: 9783030914783. This book presents how to apply recent machine learning (deep learning) methods for the task of speech quality prediction. The author shows how recent advancements in machine learning can be leveraged for the task of speech quality prediction and provides an in-depth analysis of...
Springer, 2023. — 215 p. This book covers various, leading-edge deep learning technologies. The author discusses new applications of deep learning and gives insight into the integration of deep learning with various application domains, such as autonomous driving, augmented reality, AIOT, 5G, and beyond.
Fullstack.io, 2020. — 769 p. Zero to Deep Learning is carefully designed to teach you step-by-step how to build, train, evaluate, improve and deploy deep learning models. Each chapter covers a topic and we provide full code examples as executable Jupyter notebooks. Within the first few minutes, we’ll know enough deep learning to start seeing the benefits of using it in our...
Fullstack.io, 2020. — 769 p. Code files only! Zero to Deep Learning is carefully designed to teach you step-by-step how to build, train, evaluate, improve and deploy deep learning models. Each chapter covers a topic and we provide full code examples as executable Jupyter notebooks. Within the first few minutes, we’ll know enough deep learning to start seeing the benefits of...
Leanpub, 2022. — 364 p. Version: 2022-10-03. Build Deep Learning applications with Keras and TensorFlow. Topics covered include Convolutional Neural networks, Recurrent Neural Networks, TensorBoard, Transfer learning, custom training loops, and Keras Functional API. Deep Learning (DL) is a branch of Machine Learning (ML) that involves building networks that try to mimic the...
Springer, 2021. — 405 p. — ISBN: 978-3030746964. This book provides an overview of different deep learning-based methods for face recognition and related problems. Specifically, the authors present methods based on autoencoders, restricted Boltzmann machines, and deep convolutional neural networks for face detection, localization, tracking, recognition, etc. The authors also...
Springer, 2021. — 354 p. — ISBN: 978-3-030-75177-7. This is the first book on synthetic data for deep learning, and its breadth of coverage may render this book as the default reference on synthetic data for years to come. The book can also serve as an introduction to several other important subfields of machine learning that are seldom touched upon in other books. Machine...
CRC Press, 2022. — 274 p. — eBook ISBN: 978-1-003-21514-1. The rise in living standards increases the expectation of people in almost every field. At the forefront is health. Over the past few centuries, there have been major developments in healthcare. Medical device technology and developments in artificial intelligence (AI) are among the most important ones. The improving...
Packt, 2022. — 321 p. — ISBN: 180324366X. Supercharge your skills for tailoring deep-learning models and deploying them in production environments with ease and precision. Key Features Learn how to convert a deep learning model running on notebook environments into a production-ready application supporting various deployment environments. Learn conversion between PyTorch and...
Birmingham: Packt Publishing, 2022. — 321 p. — ISBN: 180324366X. Supercharge your skills for tailoring deep-learning models and deploying them in production environments with ease and precision. Key Features Learn how to convert a deep learning model running on notebook environments into a production-ready application supporting various deployment environments. Learn conversion...
Pragmatic Programmers, LLC., 2020. — 342 p. — ISBN: 978-1680506600. You’ve decided to tackle machine learning – because you’re job hunting, embarking on a new project, or just think self-driving cars are cool. But where to start? It’s easy to be intimidated, even as a software developer. The good news is that it doesn’t have to be that hard. Master machine learning by writing...
Academic Press, 2021. — 292 p. — ISBN: 978-0-12-822226-3. Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful computational models....
Singapore: Springer, 2022. — 406 p. Deep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously considered to be very...
Academic Press/Elsevier, 2023. — 303 p. Diagnostic Biomedical Signal and Image Processing Applications with Deep Learning Methods present comprehensive research on both medical imaging and medical signals analysis. The book discusses classification, segmentation, detection, tracking, and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT, and...
New York: Springer, 2021. — 293 p. This book provides a complete illustration of deep learning concepts with case-studies and practical examples useful for real time applications. This book introduces a broad range of topics in deep learning. The authors start with the fundamentals, architectures, tools needed for effective implementation for scientists. They then present...
Springer, 2021. — 293 p. — (EAI/Springer Innovations in Communication and Computing). — ISBN: 978-3-030-66518-0. This book provides a complete illustration of deep learning concepts with case-studies and practical examples useful for real time applications. This book introduces a broad range of topics in deep learning. The authors start with the fundamentals, architectures,...
Amazon Digital Services LLC, 2019. — 89 p. — ASIN B07GTT616W. Want to learn deep learning and AI, but hate math? This book is an experiment for me. After years of teaching successful deep learning and machine learning courses online, I’ve come to notice a few patterns. One of them is that a large subset of students just RUN AWAY at the sight of math. This is somewhat...
GitforGits, 2024. — 332 p. — ASIN: B0DM3K9NPC. This is the practical, solution-oriented book for every data scientist, machine learning engineer, and AI engineer to utilize the most of Google JAX for efficient and advanced machine learning. It covers essential tasks, troubleshooting scenarios, and optimization techniques to address common challenges encountered while working...
O’Reilly, 2019. — 253 p. — ISBN13: 978-1-492-03983-9 Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields....
2nd Edition. — Packt Publishing, 2020. — 760 p. - ISBN: 9781839210686 Master classic PL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow With significant enhancement in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning...
Independently published, 2023. — 131 p. — ASIN B0CQTJ4GNZ. About the technology: Forget predictable pixels and soulless algorithms. GANs, the renegade artists of AI, paint beyond the canvas, blurring reality with their brushstrokes of code. This book is your passport to their rebellion. GANs & TensorFlow for Developers is your code-fueled escape hatch from the ordinary. Harness...
Packt, 2020. — 432 p. — ISBN: 9781838640859. Implementing supervised, unsupervised, and generative deep learning (DL) models using Keras, TensorFlow, and PyTorch Key Features Understand the fundamental machine learning concepts useful in deep learning Learn the underlying mathematical and statistical concepts as you implement smart deep learning models from scratch Explore...
Morgan & Claypool, 2020. — 236 p. — (Synthesis Lectures on Computer Architecture). — ISBN10: 1681739666, 13 978-1681739663. This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications. The purpose of this book is to provide a solid understanding of (1) the design,...
Boca Raton: CRC Press, 2024. — 217 p. The book aims to highlight the potential of Deep Learning (DL)-)-enabled methods in intelligent fault diagnosis (IFD), along with their benefits and contributions. The authors first introduce basic applications of DL-enabled IFD, including auto-encoders, deep belief networks, and convolutional neural networks. Advanced topics of DL-enabled...
Independently published, 2019. — 180 p. — ISBN: 1709576030. What if you could teach your computer how to learn the way the human brain does? And what if you could do that even without having any background in programming? If you think that this is something that may have a huge impact on your life please keep reading, because you are right… it is! If you are reading this you...
Manning, 2021. - 264p. - ISBN: 9781617296727. Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases. Deep learning offers the potential to identify complex patterns and relationships hidden in data of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis...
Manning Publications, 2020. — 266 p. — ISBN: 1617296724, 9781617296727. Deep learning offers the potential to identify complex patterns and relationships hidden in data of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis techniques to the kind of structured, tabular data you’ll find in the relational databases that real-world...
Manning Publications Co., 2020. — 241 p. — ISBN: 978-1617296727. Deep learning offers the potential to identify complex patterns and relationships hidden in data of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis techniques to the kind of structured, tabular data you'll find in the relational databases that real-world...
Packt Publishing, 2021. — 317 p. — ISBN 9781800206137. Discover ways to implement various deep learning algorithms by leveraging Python and other technologies Key Features Learn deep learning models through several activities Begin with simple machine learning problems, and finish by building a complex system of your own Teach your machines to see by mastering the technologies...
Packt Publishing, 2021. — 317 p. — ISBN 9781800206137. Code Files Only! Discover ways to implement various deep learning algorithms by leveraging Python and other technologies Key Features Learn deep learning models through several activities Begin with simple machine learning problems, and finish by building a complex system of your own Teach your machines to see by mastering...
Springer Cham 2021. — 104 p. — ISBN: 978-3-030-94482-7. The book represents the first attempt to systematically deal with the use of deep neural networks to forecast chaotic time series. Different from most of the current literature, it implements a multi-step approach, i.e., the forecast of an entire interval of future values. This is relevant for many applications, such as...
Wiley-IEEE Press, 2024. — 259 p. — ISBN: 978-1394205608. Discover how to train Deep Learning models by learning how to build real Deep Learning software libraries and verification software! The study of Deep Learning and Artificial Neural Networks (ANN) is a significant subfield of artificial intelligence (AI) that can be found within numerous fields: medicine, law, financial...
Birmingham: Packt Publishing, 2022. — 363 p. — ISBN: 180056161X. Build, train, deploy, and scale deep learning models quickly and accurately, improving your productivity using the lightweight PyTorch Wrapper. Key Features Become well-versed with PyTorch Lightning architecture and learn how it can be implemented in various industry domains. Speed up your research using PyTorch...
Springer Cham, 2023. — 661 p. — eBook ISBN: 978-3-031-29555-3. This book discusses Artificial Neural Networks (ANN) and their ability to predict outcomes using deep and shallow learning principles. The author first describes ANN implementation, consisting of at least three layers that must be established together with cells, one of which is input, the other is output, and the...
IGI Global, 2020. — 346 p. — ISBN: 9781799821090. Deep learning includes a subset of machine learning for processing the unsupervised data with artificial neural network functions. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. When applied to engineering, deep learning can have a great...
Wiley-Scrivener, 2024. — 496 p. — ISBN: 978-1394214303. This book provides a comprehensive overview of current research and developments in the field of deep learning models for stock market forecasting in the developed and developing worlds. The book delves into the realm of deep learning and embraces the challenges, opportunities, and transformation of stock market analysis....
Springer Cham, 2023. — 161 p. — (Springer Handbooks) — eBook ISBN: 978-3-031-39244-3. An easy-to-understand description of the multiple facets of design, development, and deployment of deep learning networks. Practical tools that facilitate understanding of underlying technology. Covers wide-ranging conceptual modeling and programming tools that animate deep learning...
Springer, 2023. — 172 p. — ISBN: 3031392434. This textbook presents multiple facets of the design, development, and deployment of deep learning networks for both students and industry practitioners. It introduces a deep learning tool set with deep learning concepts interwoven to enhance understanding. It also presents the design and technical aspects of programming along with a...
Springer, 2023. — 483 p. This book is a comprehensive curation, exposition, and illustrative discussion of recent research tools for the interpretability of Deep Learning models, with a focus on neural network architectures. In addition, it includes several case studies from application-oriented articles in the fields of Computer Vision, optics, and Machine Learning related...
New York: Springer, 2021. — 212 p. This book provides readers with a comprehensive and recent exposition in deep learning and its multidisciplinary applications, with a concentration on advances of deep learning architectures. The book discusses various artificial intelligence (AI) techniques based on deep learning architecture with applications in natural language processing,...
New York: Amazon.com Services LLC, 2020. — 264 p. This book will help you to understand the realms of Deep Learning from A-Z. In today’s modern world, Deep Learning has taken over the reins of Machine Learning and Artificial Intelligence. We are all familiar with the term “Deep Learning.” But have you ever wondered what it really is? This book will cover all the major features...
CRC Press, 2022. — 217 p. — ISBN: 978-1-032-10446-1. Data science revolves around two giants: Big Data analytics and Deep Learning. It is becoming challenging to handle and retrieve useful information due to how fast data is expanding. This book presents the technologies and tools to simplify and streamline the formation of Big Data as well as Deep Learning systems. This book...
Berlin: de Gruyter, 2022. — 214 p. The book series "Smart Computing Applications" provides a platform for researchers, academicians, and practitioners to exchange ideas on current theoretical and applied data science and computing technologies research, with particular attention to the possible applications of such technologies in the industry, especially in the field of...
De Gruyter, 2022. — 214 p. — ISBN: 978-3-11-075061-4. Cognitive computing simulates human thought processes with self-learning algorithms that utilize data mining, pattern recognition, and natural language processing (NLP). The integration of Deep Learning (DL) improves the performance of Cognitive computing systems in many applications, helping in utilizing heterogeneous data...
Hershey: IGI Global, 2020. — 310 p. The field of healthcare is seeing a rapid expansion of technological advancement within current medical practices. The implementation of technologies including neural networks, multi-model imaging, genetic algorithms, and soft computing are assisting in predicting and identifying diseases, diagnosing cancer, and the examination of cells....
IGI Global, 2023. — 400 p. — ISBN-13: 978-1668480984. Today’s business world is changing with the adoption of the Internet of Things (IoT). IoT is helping in prominently capturing a tremendous amount of data from multiple sources. Realizing the future and full potential of IoT devices will require an investment in new technologies. The Handbook of Research on Deep Learning...
IGI Global, 2023. — 400 p. — ISBN-13: 978-1668480984. Today’s business world is changing with the adoption of the Internet of Things (IoT). IoT is helping in prominently capturing a tremendous amount of data from multiple sources. Realizing the future and full potential of IoT devices will require an investment in new technologies. The Handbook of Research on Deep Learning...
BPB Publications, 2023. — 878 p. The book presents you with a thorough introduction to AI and Machine learning, starting from the basics and progressing to a comprehensive coverage of Deep Learning with Python. You will be introduced to the intuition of Neural Networks and how to design and train them effectively. Moving on, you will learn how to use Convolutional Neural...
J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Pandian Vasant. — IGI Global, 2020. — 355 p. — ISBN: 978-1799811947 (ebook). Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this...
Independently published, 2022. — 287 p. This document contains a practical and comprehensive introduction to everything related to deep learning in the context of physical simulations. As much as possible, all topics come with hands-on code examples in the form of Jupyter notebooks to quickly get started. Beyond standard supervised learning from data, we’ll look at physical...
John Wiley & Sons, 2023. — 544 p. — ISBN: 978-1-119-84502-7. A concise and practical exploration of key topics and applications in data science. In Deep Learning: From Big Data to Artificial Intelligence with R, expert researcher Dr. Stéphane Tufféry delivers an insightful discussion of the applications of deep learning and big data that focuses on practical instructions on...
John Wiley & Sons, 2023. — 544 p. — ISBN: 978-1-119-84502-7. A concise and practical exploration of key topics and applications in data science In Deep Learning: From Big Data to Artificial Intelligence with R, expert researcher Dr. Stéphane Tufféry delivers an insightful discussion of the applications of deep learning and big data that focuses on practical instructions on...
CRC Press, 2023. — 140 p. — ISBN: 9781003091356. Deep learning is an artificially intelligent entity that teaches itself and can be utilized to make predictions. Deep learning mimics the human brain and provides learning solutions addressing many challenging problems in the area of visual computing. From object recognition to image classification for diagnostics, deep learning...
Boca Raton: CRC Press, 2022 — 307 p. Deep Learning: A Comprehensive Guide provides comprehensive coverage of Deep Learning and Machine Learning concepts. Deep Learning and Machine Learning are the most sought-after domains, require a deep understanding and this book gives no less than that. This book enables the reader to build innovative and useful applications based on ML and...
CRC Press, 2022 — 307 p. — ISBN: 1032028823. Deep Learning: A Comprehensive Guide provides comprehensive coverage of Deep Learning and Machine Learning concepts. Deep Learning and Machine Learning are the most sought-after domains, require a deep understanding and this book gives no less than that. This book enables the reader to build innovative and useful applications based...
Packt, 2022. — 188 p. — ISBN: 9781801816823. Search is a big and ever-growing part of the tech ecosystem. Traditional search, however, has limitations that are hard to overcome because of the way it is designed. A neural search is a novel approach that uses the power of machine learning to retrieve information using vector embeddings as first-class citizens, opening up new...
Manning Publications Co., 2023. — 362 p. — ISBN: 978-1633439863. A vital guide to building the platforms and systems that bring deep learning models to production. In Designing Deep Learning Systems you will learn how to: Transfer your software development skills to deep learning systems. Recognize and solve common engineering challenges for deep learning systems. Understand...
Birmingham: Packt Publishing, 2022. — 283 p. — ISBN: 1801815690. Build and deploy an efficient data processing pipeline for machine learning model training in an elastic, in-parallel model training or multi-tenant cluster and cloud. Key Features . Accelerate model training and interference with order-of-magnitude time reduction. Learn state-of-the-art parallel schemes for both...
World Scientific Publishing, 2021. — 641 p. — ISBN: 9811234051. This unique compendium discusses some core ideas for the development and implementation of machine learning from three different perspectives — the statistical perspective, the artificial neural network perspective, and the deep learning methodology. The useful reference text represents a solid foundation in...
New York: Amazon.com Services LLC, 2020. — 181 p. This book discusses the intricacies of the internal workings of a deep learning model. It addresses the techniques and methods that can not only boost the productivity of your machine learning architectural skills, but also introduces new concepts. Implemented correctly, these can set your deep learning modela league apart from...
GitforGits, 2023. — 118 p. — ISBN-13: 978-8196288358. “Google JAX Essentials” is a comprehensive guide designed for machine learning and deep learning professionals aiming to leverage the power and capabilities of Google’s JAX library in their projects. Throughout eight chapters, this book takes the reader from understanding the challenges of deep learning and numerical...
GitforGits, 2023. — 118 p. — ISBN-13: 978-8196288358. “Google JAX Essentials” is a comprehensive guide designed for machine learning and deep learning professionals aiming to leverage the power and capabilities of Google’s JAX library in their projects. Throughout eight chapters, this book takes the reader from understanding the challenges of deep learning and numerical...
New York: Springer, 2022. — 103 p. The volume of data being collected in solar astronomy has exponentially increased over the past decade and we will be entering the age of petabyte solar data. Deep learning has been an invaluable tool exploited to efficiently extract key information from the massive solar observation data, to solve the tasks of data archiving/classification,...
World Scientific Publishing, 2023. — 309 p. — ISBN: 9789811266928. Deep learning, first introduced by Hinton et al. in 2006,1 has brought great changes to the world. Modern deep learning technique relies upon deep neuron networks (DNN), which have a very long history and aim to mimic the functionality of human brains. After more than 15 years of development, it has become a...
Springer, 2023. — 408 p. This book is intended for students, engineers, and researchers interested in both computational mechanics and Deep Learning. It presents the mathematical and computational foundations of Deep Learning (DL) with detailed mathematical formulas in an easy-to-understand manner. It also discusses various applications of Deep Learning in Computational...
Apress, 2021. - 463 p. - ISBN: 1484274121 Learn how to harness modern deep-learning methods in many contexts. Packed with intuitive theory, practical implementation methods, and deep-learning case studies, this book reveals how to acquire the tools you need to design and implement like a deep-learning architect. It covers tools deep learning engineers can use in a wide range of...
Manning Publications, 2020. — 384 p. — ISBN: 9781617295430. Humans learn best from feedback — we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement...
CRC Press, 2023. — 107 p. Computer vision-based crack-like object detection has many useful applications, such as inspecting/monitoring pavement surfaces, underground pipelines, bridge cracks, railway tracks, etc. However, in most contexts, cracks appear as thin, irregular long-narrow objects, and often are buried in a complex, textured background with high diversity which...
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