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

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.

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

Table of Contents

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

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

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

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

Part II: Essential Theory Illustrated 73

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

Prerequisites 75

Installation 76

A Shallow Network in Keras 76

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

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

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

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

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

Appendix A: Formal Neural Network Notation 333

Appendix B: Backpropagation 335

PyTorch Features 339

PyTorch in Practice 341

“This book is a stunning achievement, written with precision and depth of understanding. It entertains you and gives you lots of interesting information at the same time. I could never imagine understanding and gaining scientific knowledge, namely ‘Deep Learning’ can be this much fun! Reading the book is a pleasure and I highly recommend it.”

- O’Reilly Online Learning (Safari) Reviewer

“This title is a great resource for those looking to understand deep learning. The illustrations are helpful and aid in cementing a richer understanding of the content, and the background context surrounding biological motivations for the tools and techniques enables a greater appreciation of the field. I enthusiastically recommend this book to any and all who are interested in the topic of deep learning.”

- O’Reilly Online Learning (Safari) Reviewer



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