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Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence

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Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence

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  • 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 todays 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
  • Ancillary resources include PowerPoint lecture slides and Instructor Notes, downloadable from the Pearson Instructor Resource Center


  • Copyright 2020
  • Dimensions: 7" x 9-1/8"
  • Pages: 416
  • Edition: 1st
  • Book
  • ISBN-10: 0-13-511669-4
  • ISBN-13: 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 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 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.


Author's Site

Please visit the authors' site at deeplearningillustrated.com.

Sample Content

Online Sample Chapter

Training Deep Networks

Sample Pages

Download the sample pages (includes Chapter 8).

Table of Contents

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
Popular Deep Reinforcement Learning Environments 68
Three Categories of AI 71
Summary 72

Part II: Essential Theory Illustrated 73

Chapter 5: The (Code) Cart Ahead of the (Theory)

Horse 75
Prerequisites 75
Installation 76
A Shallow Network in Keras 76
Summary 84

Chapter 6: Artificial Neurons Detecting Hot Dogs 85
Biological Neuroanatomy 101 85
The Perceptron 86
Modern Neurons and Activation Functions 91
Choosing a Neuron 96
Summary 96
Key Concepts 97

Chapter 7: Artificial Neural Networks 99
The Input Layer 99
Dense Layers 99
A Hot Dog-Detecting Dense Network 101
The Softmax Layer of a Fast Food-Classifying Network 106
Revisiting Our Shallow Network 108
Summary 110
Key Concepts 110

Chapter 8: Training Deep Networks 111
Cost Functions 111
Optimization: Learning to Minimize Cost 115
Backpropagation 124
Tuning Hidden-Layer Count and Neuron
Count 125
An Intermediate Net in Keras 127
Summary 129
Key Concepts 130

Chapter 9: Improving Deep Networks 131
Weight Initialization 131
Unstable Gradients 137
Model Generalization (Avoiding Overfitting) 140
Fancy Optimizers 145
A Deep Neural Network in
Keras 147
Regression 149
TensorBoard 152
Summary 154
Key Concepts 155

Part III: Interactive Applications of Deep Learning 157

Chapter 10: Machine Vision 159

Convolutional Neural Networks 159
Pooling Layers 169
LeNet-5 in Keras 171
AlexNet and VGGNet in Keras 176
Residual Networks 179
Applications of Machine Vision 182
Summary 193
Key Concepts 193

Chapter 11: Natural Language Processing 195
Preprocessing Natural Language Data 195
Creating Word Embeddings with word2vec 206
The Area under the ROC Curve 217
Natural Language Classification with Familiar Networks 222
Networks Designed for Sequential Data 240
Non-sequential Architectures: The Keras Functional API 251
Summary 256
Key Concepts 257

Chapter 12: Generative Adversarial Networks 259
Essential GAN Theory 259
The Quick, Draw! Dataset 263
The Discriminator Network 266
The Generator Network 269
The Adversarial Network 272
GAN Training 275
Summary 281
Key Concepts 282

Chapter 13: Deep Reinforcement Learning 283
Essential Theory of Reinforcement Learning 283
Essential Theory of Deep Q-Learning Networks 290
Defining a DQN Agent 293
Interacting with an OpenAI Gym Environment 300
Hyperparameter Optimization with SLM Lab 303
Agents Beyond DQN 306
Summary 308
Key Concepts 309

Part IV: You and AI 311

Chapter 14:  Moving Forward with Your Own Deep Learning Projects 313

Ideas for Deep Learning Projects 313
Resources for Further Projects 317
The Modeling Process, Including Hyperparameter Tuning 318
Deep Learning Libraries 321
Software 2.0 324
Approaching Artificial General Intelligence 326
Summary 328

Part V: Appendices 331

Appendix A: Formal Neural Network Notation 333

Appendix B: Backpropagation 335

Appendix C: PyTorch 339

PyTorch Features 339
PyTorch in Practice 341

Index 345



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