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...
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...
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, 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...
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 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...
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....
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...
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, 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...
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...
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...
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. — 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...
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...
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...
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...
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...
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...
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: 978-981-10-5151-7. 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...
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...
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...
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...
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...
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...
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...
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...
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....
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...
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...
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...
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...
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...
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...
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...
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...
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,...
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...
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...
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...
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