A richly-illustrated, full-color introduction to deep learning that offers visual and conceptual explanations instead of equations. You’ll learn how to use key deep learning algorithms without the need for complex math.
Ever since computers began beating us at chess, they’ve been getting better at a wide range of human activities, from writing songs and generating news articles to helping doctors provide healthcare.
Deep learning is the source of many of these breakthroughs, and its remarkable ability to find patterns hiding in data has made it the fastest growing field in artificial intelligence (AI). Digital assistants on our phones use deep learning to understand and respond intelligently to voice commands; automotive systems use it to safely navigate road hazards; online platforms use it to deliver personalized suggestions for movies and books – the possibilities are endless.
Deep Learning: A Visual Approach is for anyone who wants to understand this fascinating field in depth, but without any of the advanced math and programming usually required to grasp its internals. If you want to know how these tools work, and use them yourself, the answers are all within these pages. And, if you’re ready to write your own programs, there are also plenty of supplemental Python notebooks in the accompanying Github repository to get you going.
The book’s conversational style, extensive color illustrations, illuminating analogies, and real-world examples expertly explain the key concepts in deep learning, including:
- How text generators create novel stories and articles
- How deep learning systems learn to play and win at human games
- How image classification systems identify objects or people in a photo
- How to think about probabilities in a way that’s useful to everyday life
- How to use the machine learning techniques that form the core of modern AI
Intellectual adventurers of all kinds can use the powerful ideas covered in Deep Learning: A Visual Approach to build intelligent systems that help us better understand the world and everyone who lives in it. It’s the future of AI, and this book allows you to fully envision it.
Cover Title Page Copyright Dedication About the Author Acknowledgments Introduction Who This Book Is For This Book Has No Complex Math and No Code There Is Code, If You Want It The Figures Are Available, Too! Errata About This Book Part I: Foundational Ideas Part II: Basic Machine Learning Part III: Deep Learning Basics Part IV: Beyond the Basics Final Words Part I: Foundational Ideas Chapter 1: An Overview of Machine Learning Expert Systems Supervised Learning Unsupervised Learning Reinforcement Learning Deep Learning Summary Chapter 2: Essential Statistics Describing Randomness Random Variables and Probability Distributions Some Common Distributions Continuous Distributions Discrete Distributions Collections of Random Values Expected Value Dependence Independent and Identically Distributed Variables Sampling and Replacement Selection with Replacement Selection Without Replacement Bootstrapping Covariance and Correlation Covariance Correlation Statistics Don’t Tell Us Everything High-Dimensional Spaces Summary Chapter 3: Measuring Performance Different Types of Probability Dart Throwing Simple Probability Conditional Probability Joint Probability Marginal Probability Measuring Correctness Classifying Samples The Confusion Matrix Characterizing Incorrect Predictions Measuring Correct and Incorrect Accuracy Precision Recall Precision-Recall Tradeoff Misleading Measures f1 Score About These Terms Other Measures Constructing a Confusion Matrix Correctly Summary Chapter 4: Bayes’ Rule Frequentist and Bayesian Probability The Frequentist Approach The Bayesian Approach Frequentists vs. Bayesians Frequentist Coin Flipping Bayesian Coin Flipping A Motivating Example Picturing the Coin Probabilities Expressing Coin Flips as Probabilities Bayes’ Rule Discussion of Bayes’ Rule Bayes’ Rule and Confusion Matrices Repeating Bayes’ Rule The Posterior-Prior Loop The Bayes Loop in Action Multiple Hypotheses Summary Chapter 5: Curves and Surfaces The Nature of Functions The Derivative Maximums and Minimums Tangent Lines Finding Minimums and Maximums with Derivatives The Gradient Water, Gravity, and the Gradient Finding Maximums and Minimums with Gradients Saddle Points Summary Chapter 6: Information Theory Surprise and Context Understanding Surprise Unpacking Context Measuring Information Adaptive Codes Speaking Morse Customizing Morse Code Entropy Cross Entropy Two Adaptive Codes Using the Codes Cross Entropy in Practice Kullback–Leibler Divergence Summary Part II: Basic Machine Learning Chapter 7: Classification Two-Dimensional Binary Classification 2D Multiclass Classification Multiclass Classification One-Versus-Rest One-Versus-One Clustering The Curse of Dimensionality Dimensionality and Density High-Dimensional Weirdness Summary Chapter 8: Training and Testing Training Testing the Performance Test Data Validation Data Cross-Validation k-Fold Cross-Validation Summary Chapter 9: Overfitting and Underfitting Finding a Good Fit Overfitting Underfitting Detecting and Addressing Overfitting Early Stopping Regularization Bias and Variance Matching the Underlying Data High Bias, Low Variance Low Bias, High Variance Comparing Curves Fitting a Line with Bayes’ Rule Summary Chapter 10: Data Preparation Basic Data Cleaning The Importance of Consistency Types of Data One-Hot Encoding Normalizing and Standardizing Normalization Standardization Remembering the Transformation Types of Transformations Slice Processing Samplewise Processing Featurewise Processing Elementwise Processing Inverse Transformations Information Leakage in Cross-Validation Shrinking the Dataset Feature Selection Dimensionality Reduction Principal Component Analysis PCA for Simple Images PCA for Real Images Summary Chapter 11: Classifiers Types of Classifiers k-Nearest Neighbors Decision Trees Using Decision Trees Overfitting Trees Splitting Nodes Support Vector Machines The Basic Algorithm The SVM Kernel Trick Naive Bayes Comparing Classifiers Summary Chapter 12: Ensembles Voting Ensembles of Decision Trees Bagging Random Forests Extra Trees Boosting Summary Part III: Deep Learning Basics Chapter 13: Neural Networks Real Neurons Artificial Neurons The Perceptron Modern Artificial Neurons Drawing the Neurons Feed-Forward Networks Neural Network Graphs Initializing the Weights Deep Networks Fully Connected Layers Tensors Preventing Network Collapse Activation Functions Straight-Line Functions Step Functions Piecewise Linear Functions Smooth Functions Activation Function Gallery Comparing Activation Functions Softmax Summary Chapter 14: Backpropagation A High-Level Overview of Training Punishing Error A Slow Way to Learn Gradient Descent Getting Started Backprop on a Tiny Neural Network Finding Deltas for the Output Neurons Using Deltas to Change Weights Other Neuron Deltas Backprop on a Larger Network The Learning Rate Building a Binary Classifier Picking a Learning Rate An Even Smaller Learning Rate Summary Chapter 15: Optimizers Error as a 2D Curve Adjusting the Learning Rate Constant-Sized Updates Changing the Learning Rate over Time Decay Schedules Updating Strategies Batch Gradient Descent Stochastic Gradient Descent Mini-Batch Gradient Descent Gradient Descent Variations Momentum Nesterov Momentum Adagrad Adadelta and RMSprop Adam Choosing an Optimizer Regularization Dropout Batchnorm Summary PART IV: Beyond the Basics Chapter 16: Convolutional Neural Networks Introducing Convolution Detecting Yellow Weight Sharing Larger Filters Filters and Features Padding Multidimensional Convolution Multiple Filters Convolution Layers 1D Convolution 1×1 Convolutions Changing Output Size Pooling Striding Transposed Convolution Hierarchies of Filters Simplifying Assumptions Finding Face Masks Finding Eyes, Noses, and Mouths Applying Our Filters Summary Chapter 17: Convnets in Practice Categorizing Handwritten Digits VGG16 Visualizing Filters, Part 1 Visualizing Filters, Part 2 Adversaries Summary Chapter 18: Autoencoders Introduction to Encoding Lossless and Lossy Encoding Blending Representations The Simplest Autoencoder A Better Autoencoder Exploring the Autoencoder A Closer Look at the Latent Variables The Parameter Space Blending Latent Variables Predicting from Novel Input Convolutional Autoencoders Blending Latent Variables Predicting from Novel Input Denoising Variational Autoencoders Distribution of Latent Variables Variational Autoencoder Structure Exploring the VAE Working with the MNIST Samples Working with Two Latent Variables Producing New Input Summary Chapter 19: Recurrent Neural Networks Working with Language Common Natural Language Processing Tasks Transforming Text into Numbers Fine-Tuning and Downstream Networks Fully Connected Prediction Testing Our Network Why Our Network Failed Recurrent Neural Networks Introducing State Rolling Up Our Diagram Recurrent Cells in Action Training a Recurrent Neural Network Long Short-Term Memory and Gated Recurrent Networks Using Recurrent Neural Networks Working with Sunspot Data Generating Text Different Architectures Seq2Seq Summary Chapter 20: Attention and Transformers Embedding Embedding Words ELMo Attention A Motivating Analogy Self-Attention Q/KV Attention Multi-Head Attention Layer Icons Transformers Skip Connections Norm-Add Positional Encoding Assembling a Transformer Transformers in Action BERT and GPT-2 BERT GPT-2 Generators Discussion Data Poisoning Summary Chapter 21: Reinforcement Learning Basic Ideas Learning a New Game The Structure of Reinforcement Learning Step 1: The Agent Selects an Action Step 2: The Environment Responds Step 3: The Agent Updates Itself Back to the Big Picture Understanding Rewards Flippers L-Learning The Basics The L-Learning Algorithm Testing Our Algorithm Handling Unpredictability Q-Learning Q-Values and Updates Q-Learning Policy Putting It All Together The Elephant in the Room Q-learning in Action SARSA The Algorithm SARSA in Action Comparing Q-Learning and SARSA The Big Picture Summary Chapter 22: Generative Adversarial Networks Forging Money Learning from Experience Forging with Neural Networks A Learning Round Why Adversarial? Implementing GANs The Discriminator The Generator Training the GAN GANs in Action Building a Discriminator and Generator Training Our Network Testing Our Network DCGANs Challenges Using Big Samples Modal Collapse Training with Generated Data Summary Chapter 23: Creative Applications Deep Dreaming Stimulating Filters Running Deep Dreaming Neural Style Transfer Representing Style Representing Content Style and Content Together Running Style Transfer Generating More of This Book Summary Final Thoughts References Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Chapter 10 Chapter 11 Chapter 12 Chapter 13 Chapter 14 Chapter 15 Chapter 16 Chapter 17 Chapter 18 Chapter 19 Chapter 20 Chapter 21 Chapter 22 Chapter 23 Image Credits Chapter 1 Chapter 10 Chapter 16 Chapter 17 Chapter 18 Chapter 23 Index
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