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...
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...
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...
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. — 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...
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...
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...
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. — 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....
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...
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...
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...
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...
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...
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...
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...
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,...
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...
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,...
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...
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....
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...
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....
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...
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...
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...
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...
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...
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,...
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 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 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...
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...
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...
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...
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...
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...
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...
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...
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,...
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...
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...
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...
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...
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,...
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...
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++....
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...
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...
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. — 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. — 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, 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...
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...
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),...
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...
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...
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...
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...
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...
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...
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,...
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...
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...
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...
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...
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...
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...
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...
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,...
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, 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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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. 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...
Comments