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

Author Grant Beyleveld , Aglaé Bassens , Jon Krohn
Release Date: 2019/09/01
ISBN: 9780135116821
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Book Description

"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 projects

Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Table of Contents

  1. Cover
  2. About This E-Book
  3. Praise for Deep Learning Illustrated
  4. Half Title
  5. Series Page
  6. Title Page
  7. Copyright Page
  8. Dedication Page
  9. Contents
  10. Figures
  11. Tables
  12. Examples
  13. Foreword
  14. Preface
    1. How to Read This Book
  15. Acknowledgments
  16. About the Authors
  17. I: Introducing Deep Learning
    1. 1. Biological and Machine Vision
      1. Biological Vision
      2. Machine Vision
      3. TensorFlow Playground
      4. Quick, Draw!
      5. Summary
    2. 2. Human and Machine Language
      1. Deep Learning for Natural Language Processing
      2. Computational Representations of Language
      3. Elements of Natural Human Language
      4. Google Duplex
      5. Summary
    3. 3. Machine Art
      1. A Boozy All-Nighter
      2. Arithmetic on Fake Human Faces
      3. Style Transfer: Converting Photos into Monet (and Vice Versa)
      4. Make Your Own Sketches Photorealistic
      5. Creating Photorealistic Images from Text
      6. Image Processing Using Deep Learning
      7. Summary
    4. 4. Game-Playing Machines
      1. Deep Learning, AI, and Other Beasts
      2. Three Categories of Machine Learning Problems
      3. Deep Reinforcement Learning
      4. Video Games
      5. Board Games
      6. Manipulation of Objects
      7. Popular Deep Reinforcement Learning Environments
      8. Three Categories of AI
      9. Summary
  18. II: Essential Theory Illustrated
    1. 5. The (Code) Cart Ahead of the (Theory) Horse
      1. Prerequisites
      2. Installation
      3. A Shallow Network in Keras
      4. Summary
    2. 6. Artificial Neurons Detecting Hot Dogs
      1. Biological Neuroanatomy 101
      2. The Perceptron
      3. Modern Neurons and Activation Functions
      4. Choosing a Neuron
      5. Summary
      6. Key Concepts
    3. 7. Artificial Neural Networks
      1. The Input Layer
      2. Dense Layers
      3. A Hot Dog-Detecting Dense Network
      4. The Softmax Layer of a Fast Food-Classifying Network
      5. Revisiting Our Shallow Network
      6. Summary
      7. Key Concepts
    4. 8. Training Deep Networks
      1. Cost Functions
      2. Optimization: Learning to Minimize Cost
      3. Backpropagation
      4. Tuning Hidden-Layer Count and Neuron Count
      5. An Intermediate Net in Keras
      6. Summary
      7. Key Concepts
    5. 9. Improving Deep Networks
      1. Weight Initialization
      2. Unstable Gradients
      3. Model Generalization (Avoiding Overfitting)
      4. Fancy Optimizers
      5. A Deep Neural Network in Keras
      6. Regression
      7. TensorBoard
      8. Summary
      9. Key Concepts
  19. III: Interactive Applications of Deep Learning
    1. 10. Machine Vision
      1. Convolutional Neural Networks
      2. Pooling Layers
      3. LeNet-5 in Keras
      4. AlexNet and VGGNet in Keras
      5. Residual Networks
      6. Applications of Machine Vision
      7. Summary
      8. Key Concepts
    2. 11. Natural Language Processing
      1. Preprocessing Natural Language Data
      2. Creating Word Embeddings with word2vec
      3. The Area under the ROC Curve
      4. Natural Language Classification with Familiar Networks
      5. Networks Designed for Sequential Data
      6. Non-sequential Architectures: The Keras Functional API
      7. Summary
      8. Key Concepts
    3. 12. Generative Adversarial Networks
      1. Essential GAN Theory
      2. The Quick, Draw! Dataset
      3. The Discriminator Network
      4. The Generator Network
      5. The Adversarial Network
      6. GAN Training
      7. Summary
      8. Key Concepts
    4. 13. Deep Reinforcement Learning
      1. Essential Theory of Reinforcement Learning
      2. Essential Theory of Deep Q-Learning Networks
      3. Defining a DQN Agent
      4. Interacting with an OpenAI Gym Environment
      5. Hyperparameter Optimization with SLM Lab
      6. Agents Beyond DQN
      7. Summary
      8. Key Concepts
  20. IV: You and AI
    1. 14. Moving Forward with Your Own Deep Learning Projects
      1. Ideas for Deep Learning Projects
      2. Resources for Further Projects
      3. The Modeling Process, Including Hyperparameter Tuning
      4. Deep Learning Libraries
      5. Software 2.0
      6. Approaching Artificial General Intelligence
      7. Summary
  21. V: Appendices
    1. A. Formal Neural Network Notation
    2. B. Backpropagation
    3. C. PyTorch
      1. PyTorch Features
      2. PyTorch in Practice
  22. Index
  23. Credits
  24. Code Snippets
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