Deep Learning Illustrated:A Visual, Interactive Guide.pdf

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About This E-Book

Praise for Deep Learning Illustrated

Half Title

Series Page

Title Page

Dedication Page

Contents

Figures

Tables

Examples

Foreword

Preface

How to Read This Book

Acknowledgments

About the Authors

I: Introducing Deep Learning

1. Biological and Machine Vision

Biological Vision

Machine Vision

TensorFlow Playground

Quick, Draw!

Summary

2. Human and Machine Language

Deep Learning for Natural Language Processing

Computational Representations of Language

Elements of Natural Human Language

Google Duplex

Summary

3. Machine Art

A Boozy All-Nighter

Arithmetic on Fake Human Faces

Style Transfer: Converting Photos into Monet (and Vice Versa)

Make Your Own Sketches Photorealistic

Creating Photorealistic Images from Text

Image Processing Using Deep Learning

Summary

4. Game-Playing Machines

Deep Learning, AI, and Other Beasts

Three Categories of Machine Learning Problems

Deep Reinforcement Learning

Video Games

Board Games

Manipulation of Objects

Popular Deep Reinforcement Learning Environments

Three Categories of AI

Summary

II: Essential Theory Illustrated

5. The (Code) Cart Ahead of the (Theory) Horse

Prerequisites

Installation

A Shallow Network in Keras

Summary

6. Artificial Neurons Detecting Hot Dogs

Biological Neuroanatomy 101

The Perceptron

Modern Neurons and Activation Functions

Choosing a Neuron

Summary

Key Concepts

7. Artificial Neural Networks

The Input Layer

Dense Layers

A Hot Dog-Detecting Dense Network

The Softmax Layer of a Fast Food-Classifying Network

Revisiting Our Shallow Network

Summary

Key Concepts

8. Training Deep Networks

Cost Functions

Optimization: Learning to Minimize Cost

Backpropagation

Tuning Hidden-Layer Count and Neuron Count

An Intermediate Net in Keras

Summary

Key Concepts

9. Improving Deep Networks

Weight Initialization

Unstable Gradients

Model Generalization (Avoiding Overfitting)

Fancy Optimizers

A Deep Neural Network in Keras

Regression

TensorBoard

Summary

Key Concepts

III: Interactive Applications of Deep Learning

10. Machine Vision

Convolutional Neural Networks

Pooling Layers

LeNet-5 in Keras

AlexNet and VGGNet in Keras

Residual Networks

Applications of Machine Vision

Summary

Key Concepts

11. Natural Language Processing

Preprocessing Natural Language Data

Creating Word Embeddings with word2vec

The Area under the ROC Curve

Natural Language Classification with Familiar Networks

Networks Designed for Sequential Data

Non-sequential Architectures: The Keras Functional API

Summary

Key Concepts

12. Generative Adversarial Networks

Essential GAN Theory

The Quick, Draw! Dataset

The Discriminator Network

The Generator Network

The Adversarial Network

GAN Training

Summary

Key Concepts

13. Deep Reinforcement Learning

Essential Theory of Reinforcement Learning

Essential Theory of Deep Q-Learning Networks

Defining a DQN Agent

Interacting with an OpenAI Gym Environment

Hyperparameter Optimization with SLM Lab

Agents Beyond DQN

Summary

Key Concepts

IV: You and AI

14. Moving Forward with Your Own Deep Learning Projects

Ideas for Deep Learning Projects

Resources for Further Projects

The Modeling Process, Including Hyperparameter Tuning

Deep Learning Libraries

Software 2.0

Approaching Artificial General Intelligence

Summary

V: Appendices

A. Formal Neural Network Notation

B. Backpropagation

C. PyTorch

PyTorch Features

PyTorch in Practice

Index

Credits

Code Snippets

About This E-Book EPUB is an open, industry-standard format for e-books. However, support for EPUB and its many features varies across reading devices and applications. Use your device or app settings to customize the presentation to your liking. Settings that you can customize often include font, font size, single or double column, landscape or portrait mode, and figures that you can click or tap to enlarge. For additional information about the settings and features on your reading device or app, visit the device manufacturer’s Web site. Many titles include programming code or configuration examples. To optimize the presentation of these elements, view the e-book in single-column, landscape mode and adjust the font size to the smallest setting. In addition to presenting code and configurations in the reflowable text format, we have included images of the code that mimic the presentation found in the print book; therefore, where the reflowable format may compromise the presentation of the code listing, you will see a “Click here to view code image” link. Click the link to view the print-fidelity code image. To return to the previous page viewed, click the Back button on your device or app.

Praise for Deep Learning Illustrated “Over the next few decades, artificial intelligence is poised to dramatically change almost every aspect of our lives, in large part due to today’s breakthroughs in deep learning. 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, writer and illustrator of Wait But Why “This book is an approachable, practical, and broad introduction to deep learning, and the most beautifully illustrated machine learning book on the market.” —Dr. Michael Osborne, Dyson Associate Professor in Machine Learning, University of Oxford “This book should be the first stop for deep learning beginners, as it contains lots of concrete, easy-to-follow examples with corresponding tutorial videos and code notebooks. Strongly recommended.” —Dr. Chong Li, cofounder, Nakamoto & Turing Labs; adjunct professor, Columbia University “It’s hard to imagine developing new products today without thinking about enriching them with capabilities using machine learning. Deep learning in particular has many practical

applications, and this book’s intelligible clear and visual approach is helpful to anyone who would like to understand what deep learning is and how it could impact your business and life for years to come.” —Helen Altshuler, engineering leader, Google “This book leverages beautiful illustrations and amusing analogies to make the theory behind deep learning uniquely accessible. Its straightforward example code and best-practice tips empower readers to immediately apply the transformative technique to their particular niche of interest.” —Dr. Rasmus Rothe, founder, Merantix “This is an invaluable resource for anyone looking to understand what deep learning is and why it powers almost every automated application today, from chatbots and voice recognition tools to self-driving cars. The illustrations and biological explanations help bring to life a complex topic and make it easier to grasp fundamental concepts.” —Joshua March, CEO and cofounder, Conversocial; author of Message Me “Deep learning is regularly redefining the state of the art across machine vision, natural language, and sequential decision-making tasks. If you too would like to pass data through deep neural networks in order to build high- performance models, then this book—with its innovative, highly visual approach—is the ideal place to begin.” —Dr. Alex Flint, roboticist and entrepreneur

Deep Learning Illustrated

Deep Learning Illustrated A Visual, Interactive Guide to Artificial Intelligence Jon Krohn with Grant Beyleveld and Aglaé Bassens Boston • Columbus • New York • San Francisco • Amsterdam • Cape Town Dubai • London • Madrid • Milan • Munich • Paris • Montreal • Toronto • Delhi • Mexico City São Paulo • Sydney • Hong Kong • Seoul • Singapore • Taipei • Tokyo

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