Part I. The Fundamentals of Machine Learning
1. The Machine Learning Landscape. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
What Is Machine Learning? 4
Why Use Machine Learning? 4
Types of Machine Learning Systems 8
Supervised/Unsupervised Learning 8
Batch and Online Learning 15
Instance-Based Versus Model-Based Learning 18
Main Challenges of Machine Learning 24
Insufficient Quantity of Training Data 24
Nonrepresentative Training Data 26
Poor-Quality Data 27
Irrelevant Features 27
Overfitting the Training Data 28
Underfitting the Training Data 30
Stepping Back 30
Testing and Validating 31
Hyperparameter Tuning and Model Selection 32
Data Mismatch 33
Exercises 34
2. End-to-End Machine Learning Project. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Working with Real Data 38
Look at the Big Picture 39
iii
Frame the Problem 39
Select a Performance Measure 42
Check the Assumptions 45
Get the Data 45
Create the Workspace 45
Download the Data 49
Take a Quick Look at the Data Structure 50
Create a Test Set 54
Discover and Visualize the Data to Gain Insights 58
Visualizing Geographical Data 59
Looking for Correlations 62
Experimenting with Attribute Combinations 65
Prepare the Data for Machine Learning Algorithms 66
Data Cleaning 67
Handling Text and Categorical Attributes 69
Custom Transformers 71
Feature Scaling 72
Transformation Pipelines 73
Select and Train a Model 75
Training and Evaluating on the Training Set 75
Better Evaluation Using Cross-Validation 76
Fine-Tune Your Model 79
Grid Search 79
Randomized Search 81
Ensemble Methods 82
Analyze the Best Models and Their Errors 82
Evaluate Your System on the Test Set 83
Launch, Monitor, and Maintain Your System 84
Try It Out! 85
Exercises 85
3. Classi€cation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
MNIST 87
Training a Binary Classifier 90
Performance Measures 90
Measuring Accuracy Using Cross-Validation 91
Confusion Matrix 92
Precision and Recall 94
Precision/Recall Tradeoff 95
The ROC Curve 99
Multiclass Classification 102
Error Analysis 104
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Multilabel Classification 108
Multioutput Classification 109
Exercises 110
4. Training Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
Linear Regression 114
The Normal Equation 116
Computational Complexity 119
Gradient Descent 119
Batch Gradient Descent 123
Stochastic Gradient Descent 126
Mini-batch Gradient Descent 129
Polynomial Regression 130
Learning Curves 132
Regularized Linear Models 136
Ridge Regression 137
Lasso Regression 139
Elastic Net 142
Early Stopping 142
Logistic Regression 144
Estimating Probabilities 144
Training and Cost Function 145
Decision Boundaries 146
Softmax Regression 149
Exercises 153
5. Support Vector Machines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
Linear SVM Classification 155
Soft Margin Classification 156
Nonlinear SVM Classification 159
Polynomial Kernel 160
Adding Similarity Features 161
Gaussian RBF Kernel 162
Computational Complexity 163
SVM Regression 164
Under the Hood 166
Decision Function and Predictions 166
Training Objective 167
Quadratic Programming 169
The Dual Problem 170
Kernelized SVM 171
Online SVMs 174
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Exercises 175
6. Decision Trees. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
Training and Visualizing a Decision Tree 177
Making Predictions 179
Estimating Class Probabilities 181
The CART Training Algorithm 182
Computational Complexity 183
Gini Impurity or Entropy? 183
Regularization Hyperparameters 184
Regression 185
Instability 188
Exercises 189
7. Ensemble Learning and Random Forests. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191
Voting Classifiers 192
Bagging and Pasting 195
Bagging and Pasting in Scikit-Learn 196
Out-of-Bag Evaluation 197
Random Patches and Random Subspaces 198
Random Forests 199
Extra-Trees 200
Feature Importance 200
Boosting 201
AdaBoost 202
Gradient Boosting 205
Stacking 210
Exercises 213
8. Dimensionality Reduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
The Curse of Dimensionality 216
Main Approaches for Dimensionality Reduction 218
Projection 218
Manifold Learning 220
PCA 222
Preserving the Variance 222
Principal Components 223
Projecting Down to d Dimensions 224
Using Scikit-Learn 224
Explained Variance Ratio 225
Choosing the Right Number of Dimensions 225
PCA for Compression 226
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Randomized PCA 227
Incremental PCA 227
Kernel PCA 228
Selecting a Kernel and Tuning Hyperparameters 229
LLE 232
Other Dimensionality Reduction Techniques 234
Exercises 235
9. Unsupervised Learning Techniques. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237
Clustering 238
K-Means 240
Limits of K-Means 250
Using clustering for image segmentation 251
Using Clustering for Preprocessing 252
Using Clustering for Semi-Supervised Learning 254
DBSCAN 256
Other Clustering Algorithms 259
Gaussian Mixtures 260
Anomaly Detection using Gaussian Mixtures 266
Selecting the Number of Clusters 267
Bayesian Gaussian Mixture Models 270
Other Anomaly Detection and Novelty Detection Algorithms 274
Part II. Neural Networks and Deep Learning
10. Introduction to Arti€cial Neural Networks with Keras. . . . . . . . . . . . . . . . . . . . . . . . . . 277
From Biological to Artificial Neurons 278
Biological Neurons 279
Logical Computations with Neurons 281
The Perceptron 281
Multi-Layer Perceptron and Backpropagation 286
Regression MLPs 289
Classification MLPs 290
Implementing MLPs with Keras 292
Installing TensorFlow 2 293
Building an Image Classifier Using the Sequential API 294
Building a Regression MLP Using the Sequential API 303
Building Complex Models Using the Functional API 304
Building Dynamic Models Using the Subclassing API 309
Saving and Restoring a Model 311
Using Callbacks 311
Table of Contents | vii
Visualization Using TensorBoard 313
Fine-Tuning Neural Network Hyperparameters 315
Number of Hidden Layers 319
Number of Neurons per Hidden Layer 320
Learning Rate, Batch Size and Other Hyperparameters 320
Exercises 322
11. Training Deep Neural Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325
Vanishing/Exploding Gradients Problems 326
Glorot and He Initialization 327
Nonsaturating Activation Functions 329
Batch Normalization 333
Gradient Clipping 338
Reusing Pretrained Layers 339
Transfer Learning With Keras 341
Unsupervised Pretraining 343
Pretraining on an Auxiliary Task 344
Faster Optimizers 344
Momentum Optimization 345
Nesterov Accelerated Gradient 346
AdaGrad 347
RMSProp 349
Adam and Nadam Optimization 349
Learning Rate Scheduling 352
Avoiding Overfitting Through Regularization 356
ℓ1
and ℓ2
Regularization 356
Dropout 357
Monte-Carlo (MC) Dropout 360
Max-Norm Regularization 362
Summary and Practical Guidelines 363
Exercises 364
12. Custom Models and Training with TensorFlow. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367
A Quick Tour of TensorFlow 368
Using TensorFlow like NumPy 371
Tensors and Operations 371
Tensors and NumPy 373
Type Conversions 374
Variables 374
Other Data Structures 375
Customizing Models and Training Algorithms 376
Custom Loss Functions 376
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Saving and Loading Models That Contain Custom Components 377
Custom Activation Functions, Initializers, Regularizers, and Constraints 379
Custom Metrics 380
Custom Layers 383
Custom Models 386
Losses and Metrics Based on Model Internals 388
Computing Gradients Using Autodiff 389
Custom Training Loops 393
TensorFlow Functions and Graphs 396
Autograph and Tracing 398
TF Function Rules 400
13. Loading and Preprocessing Data with TensorFlow. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403
The Data API 404
Chaining Transformations 405
Shuffling the Data 406
Preprocessing the Data 409
Putting Everything Together 410
Prefetching 411
Using the Dataset With tf.keras 413
The TFRecord Format 414
Compressed TFRecord Files 415
A Brief Introduction to Protocol Buffers 415
TensorFlow Protobufs 416
Loading and Parsing Examples 418
Handling Lists of Lists Using the SequenceExample Protobuf 419
The Features API 420
Categorical Features 421
Crossed Categorical Features 421
Encoding Categorical Features Using One-Hot Vectors 422
Encoding Categorical Features Using Embeddings 423
Using Feature Columns for Parsing 426
Using Feature Columns in Your Models 426
TF Transform 428
The TensorFlow Datasets (TFDS) Project 429
14. Deep Computer Vision Using Convolutional Neural Networks. . . . . . . . . . . . . . . . . . . 431
The Architecture of the Visual Cortex 432
Convolutional Layer 434
Filters 436
Stacking Multiple Feature Maps 437
TensorFlow Implementation 439
Table of Contents | ix
Memory Requirements 441
Pooling Layer 442
TensorFlow Implementation 444
CNN Architectures 446
LeNet-5 449
AlexNet 450
GoogLeNet 452
VGGNet 456
ResNet 457
Xception 459
SENet 461
Implementing a ResNet-34 CNN Using Keras 464
Using Pretrained Models From Keras 465
Pretrained Models for Transfer Learning 467
Classification and Localization 469
Object Detection 471
Fully Convolutional Networks (FCNs) 473
You Only Look Once (YOLO) 475
Semantic Segmentation 478
Exercises 482
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