Jon Krohn is the chief data scientist at untapt, a machine learning startup in New York. He leads a flourishing Deep Learning Study Group, presents the acclaimed Deep Learning with TensorFlow LiveLessons in Safari, and teaches his Deep Learning curriculum at the NYC Data Science Academy. Jon holds a doctorate in neuroscience from Oxford University and has been publishing on machine learning in leading academic journals since 2010. Grant Beyleveld is a doctoral candidate at the Icahn School of Medicine at New York’s Mount Sinai hospital, researching the relationship between viruses and their hosts. A founding member of the Deep Learning Study Group, he holds a masters in molecular medicine and medical biochemistry from the University of Witwatersrand. Aglaé Bassens is a Belgian artist based in Brooklyn. She studied fine arts at The Ruskin School of Drawing and Fine Art, Oxford University, and University College London’s Slade School of Fine Arts. Along with her work as an illustrator, her practice includes still life painting and murals.

Texte du rabat

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 previous books on deep learning have often been non-intuitive, inaccessible, and dry. 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 illustrations, it abstracts away much of the complexity of building deep learning models, making the field more fun to learn and accessible to a far wider audience.

Part I's high-level overview explains what Deep Learning is, why it has become so ubiquitous, and how it relates to concepts and terminology such as Artificial Intelligence, Machine Learning, Artificial Neural Networks, and Reinforcement Learning. These opening chapters are replete with vivid illustrations, easy-to-grasp analogies, and character-focused narratives.

Building on this foundation, the authors then offer a practical reference and tutorial for applying a wide spectrum of proven deep learning techniques. Essential theory is covered with as little mathematics as possible and is illuminated with hands-on Python code. Theory is supported with practical "run-throughs" available in accompanying Jupyter notebooks, delivering a pragmatic understanding of all major deep learning approaches and their applications: machine vision, natural language processing, image generation, and videogaming.

To help readers accomplish more in less time, the authors feature several of today's most widely used and innovative deep learning libraries, including TensorFlow and its high-level API, Keras; PyTorch; and the recently released, high-level Coach, a TensorFlow API that abstracts away the complexity typically associated with building Deep Reinforcement Learning algorithms.

Ideal for software developers, data scientists, and analysts at all levels of experience
Teaches through simple visuals, accessible Python code examples, character-driven narratives, and intuitive analogies
Covers today's leading applications, including machine vision, natural language processing, image generation, and videogames
Introduces four powerful Deep Learning libraries: TensorFlow, Keras, PyTorch, and Coach
Carefully designed to minimize mathematical formulae and avoid unnecessary complexity

The first full-color, illustrated, hands-on guide to the fundamentals of modern, deep-learning AI: simply the most intuitive, practical way to get started

Ideal for software developers, data scientists, and analysts at all levels of experience
Teaches through simple visuals, accessible Python code examples, character-driven narratives, and intuitive analogies
Covers today's leading applications, including machine vision, natural language processing, image generation, and videogames
Introduces four powerful Deep Learning libraries: TensorFlow, Keras, PyTorch, and Coach
Carefully designed to minimize mathematical formulae and avoid unnecessary complexity

Résumé

"The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come."
—Tim Urban, author of Wait But Why

Fully Practical, Insightful Guide to Modern Deep Learning
Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn.
World-class instructor and practitioner Jon Krohn—with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens—presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered.
You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms.

Discover what makes deep learning systems unique, and the implications for practitioners
Explore new tools that make deep learning models easier to build, use, and improve
Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more
Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projectsRegister your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Contenu

Figures xix
Tables xxvii
Examples xxix
Foreword xxxiii
Preface xxxv
Acknowledgments xxxix
About the Authors xli
Part I: Introducing Deep Learning 1
Chapter 1: Biological and Machine Vision 3
Biological Vision 3
Machine Vision 8
TensorFlow Playground 17
Quick, Draw! 19
Summary 19
Chapter 2: Human and Machine Language 21
Deep Learning for Natural Language
Processing 21
Computational Representations of Language 25
Elements of Natural Human Language 33
Google Duplex 35
Summary 37
Chapter 3: Machine Art 39
A Boozy All-Nighter 39
Arithmetic on Fake Human Faces 41
Style Transfer: Converting Photos into Monet (and Vice Versa) 44
Make Your Own Sketches Photorealistic 45
Creating Photorealistic Images from Text 45
Image Processing Using Deep Learning 46
Summary 48
Chapter 4: Game-Playing Machines 49
Deep Learning, AI, and Other Beasts 49
Three Categories of Machine Learning Problems 53
Deep Reinforcement Learning 56
Video Games 57
Board Games 59
Manipulation of Objects 67
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