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About This E-Book
Praise for Deep Learning Illustrated
Half Title
Series Page
Title Page
Copyright 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