Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow - Concepts, Tools, and Techniques to Build Intelligent Systems, 2nd Edition

Machine Learning in Your Projects......Page 5
Objective and Approach......Page 6
Prerequisites......Page 7
Roadmap......Page 8
Changes in the Second Edition......Page 9
Other Resources......Page 10
Conventions Used in This Book......Page 12
Code Examples......Page 13
O’Reilly Online Learning......Page 14
How to Contact Us......Page 15
Acknowledgments......Page 16
I. The Fundamentals of Machine Learning......Page 20
1. The Machine Learning Landscape......Page 21
What Is Machine Learning?......Page 22
Why Use Machine Learning?......Page 23
Examples of Applications......Page 27
Types of Machine Learning Systems......Page 29
Supervised/Unsupervised Learning......Page 30
Batch and Online Learning......Page 39
Instance-Based Versus Model-Based Learning......Page 42
Insufficient Quantity of Training Data......Page 50
Nonrepresentative Training Data......Page 52
Poor-Quality Data......Page 54
Irrelevant Features......Page 55
Overfitting the Training Data......Page 56
Underfitting the Training Data......Page 58
Stepping Back......Page 59
Hyperparameter Tuning and Model Selection......Page 60
Data Mismatch......Page 62
Exercises......Page 63
Working with Real Data......Page 66
Look at the Big Picture......Page 68
Frame the Problem......Page 69
Select a Performance Measure......Page 71
Check the Assumptions......Page 74
Create the Workspace......Page 75
Download the Data......Page 79
Take a Quick Look at the Data Structure......Page 81
Create a Test Set......Page 85
Discover and Visualize the Data to Gain Insights......Page 91
Visualizing Geographical Data......Page 92
Looking for Correlations......Page 94
Experimenting with Attribute Combinations......Page 98
Data Cleaning......Page 100
Handling Text and Categorical Attributes......Page 104
Custom Transformers......Page 107
Feature Scaling......Page 108
Transformation Pipelines......Page 109
Training and Evaluating on the Training Set......Page 112
Better Evaluation Using Cross-Validation......Page 114
Fine-Tune Your Model......Page 116
Grid Search......Page 117
Randomized Search......Page 119
Analyze the Best Models and Their Errors......Page 120
Evaluate Your System on the Test Set......Page 121
Launch, Monitor, and Maintain Your System......Page 123
Try It Out!......Page 126
Exercises......Page 127
MNIST......Page 130
Training a Binary Classifier......Page 134
Measuring Accuracy Using Cross-Validation......Page 135
Confusion Matrix......Page 137
Precision and Recall......Page 140
Precision/Recall Trade-off......Page 141
The ROC Curve......Page 146
Multiclass Classification......Page 150
Error Analysis......Page 154
Multilabel Classification......Page 159
Multioutput Classification......Page 160
Exercises......Page 163
4. Training Models......Page 166
Linear Regression......Page 167
The Normal Equation......Page 169
Gradient Descent......Page 173
Batch Gradient Descent......Page 178
Stochastic Gradient Descent......Page 182
Mini-batch Gradient Descent......Page 186
Polynomial Regression......Page 188
Learning Curves......Page 191
Regularized Linear Models......Page 196
Ridge Regression......Page 197
Lasso Regression......Page 199
Elastic Net......Page 202
Early Stopping......Page 203
Estimating Probabilities......Page 205
Training and Cost Function......Page 207
Decision Boundaries......Page 208
Softmax Regression......Page 211
Exercises......Page 216
Linear SVM Classification......Page 219
Soft Margin Classification......Page 220
Nonlinear SVM Classification......Page 222
Polynomial Kernel......Page 224
Similarity Features......Page 225
Gaussian RBF Kernel......Page 226
Computational Complexity......Page 227
SVM Regression......Page 228
Decision Function and Predictions......Page 230
Training Objective......Page 231
Quadratic Programming......Page 233
Kernelized SVMs......Page 234
Online SVMs......Page 237
Exercises......Page 238
Training and Visualizing a Decision Tree......Page 240
Making Predictions......Page 241
Estimating Class Probabilities......Page 243
Computational Complexity......Page 244
Regularization Hyperparameters......Page 245
Regression......Page 247
Instability......Page 249
Exercises......Page 250
Voting Classifiers......Page 253
Bagging and Pasting......Page 257
Bagging and Pasting in Scikit-Learn......Page 258
Out-of-Bag Evaluation......Page 259
Random Patches and Random Subspaces......Page 260
Extra-Trees......Page 261
Feature Importance......Page 262
AdaBoost......Page 263
Gradient Boosting......Page 267
Stacking......Page 272
Exercises......Page 276
8. Dimensionality Reduction......Page 279
The Curse of Dimensionality......Page 280
Main Approaches for Dimensionality Reduction......Page 281
Projection......Page 282
Manifold Learning......Page 285
PCA......Page 286
Principal Components......Page 287
Projecting Down to d Dimensions......Page 289
Explained Variance Ratio......Page 290
Choosing the Right Number of Dimensions......Page 291
PCA for Compression......Page 292
Incremental PCA......Page 294
Kernel PCA......Page 295
Selecting a Kernel and Tuning Hyperparameters......Page 296
LLE......Page 299
Other Dimensionality Reduction Techniques......Page 301
Exercises......Page 303
9. Unsupervised Learning Techniques......Page 306
Clustering......Page 307
K-Means......Page 310
Limits of K-Means......Page 323
Using Clustering for Image Segmentation......Page 324
Using Clustering for Preprocessing......Page 326
Using Clustering for Semi-Supervised Learning......Page 328
DBSCAN......Page 332
Other Clustering Algorithms......Page 336
Gaussian Mixtures......Page 338
Anomaly Detection Using Gaussian Mixtures......Page 346
Selecting the Number of Clusters......Page 348
Bayesian Gaussian Mixture Models......Page 353
Other Algorithms for Anomaly and Novelty Detection......Page 358
Exercises......Page 360
II. Neural Networks and Deep Learning......Page 363
10. Introduction to Artificial Neural Networks with Keras......Page 364
From Biological to Artificial Neurons......Page 365
Biological Neurons......Page 366
Logical Computations with Neurons......Page 368
The Perceptron......Page 370
The Multilayer Perceptron and Backpropagation......Page 376
Regression MLPs......Page 380
Classification MLPs......Page 382
Implementing MLPs with Keras......Page 384
Installing TensorFlow 2......Page 386
Building an Image Classifier Using the Sequential API......Page 387
Building a Regression MLP Using the Sequential API......Page 401
Building Complex Models Using the Functional API......Page 402
Using the Subclassing API to Build Dynamic Models......Page 408
Saving and Restoring a Model......Page 410
Using Callbacks......Page 411
Using TensorBoard for Visualization......Page 414
Fine-Tuning Neural Network Hyperparameters......Page 418
Number of Hidden Layers......Page 423
Number of Neurons per Hidden Layer......Page 424
Learning Rate, Batch Size, and Other Hyperparameters......Page 425
Exercises......Page 428
11. Training Deep Neural Networks......Page 434
The Vanishing/Exploding Gradients Problems......Page 435
Glorot and He Initialization......Page 436
Nonsaturating Activation Functions......Page 438
Batch Normalization......Page 443
Gradient Clipping......Page 451
Reusing Pretrained Layers......Page 452
Transfer Learning with Keras......Page 455
Unsupervised Pretraining......Page 457
Pretraining on an Auxiliary Task......Page 459
Momentum Optimization......Page 460
Nesterov Accelerated Gradient......Page 462
AdaGrad......Page 464
Adam and Nadam Optimization......Page 466
Learning Rate Scheduling......Page 471
Avoiding Overfitting Through Regularization......Page 477
ℓ1 and ℓ2 Regularization......Page 478
Dropout......Page 479
Monte Carlo (MC) Dropout......Page 483
Max-Norm Regularization......Page 486
Summary and Practical Guidelines......Page 487
Exercises......Page 489
A Quick Tour of TensorFlow......Page 493
Using TensorFlow like NumPy......Page 497
Tensors and Operations......Page 498
Tensors and NumPy......Page 500
Type Conversions......Page 501
Variables......Page 502
Other Data Structures......Page 503
Custom Loss Functions......Page 504
Saving and Loading Models That Contain Custom Components......Page 506
Custom Activation Functions, Initializers, Regularizers, and Constraints......Page 508
Custom Metrics......Page 510
Custom Layers......Page 514
Custom Models......Page 518
Losses and Metrics Based on Model Internals......Page 521
Computing Gradients Using Autodiff......Page 524
Custom Training Loops......Page 529
TensorFlow Functions and Graphs......Page 533
AutoGraph and Tracing......Page 535
TF Function Rules......Page 537
Exercises......Page 539
13. Loading and Preprocessing Data with TensorFlow......Page 543
The Data API......Page 544
Chaining Transformations......Page 545
Shuffling the Data......Page 547
Preprocessing the Data......Page 551
Putting Everything Together......Page 553
Prefetching......Page 554
Using the Dataset with tf.keras......Page 556
The TFRecord Format......Page 558
A Brief Introduction to Protocol Buffers......Page 559
TensorFlow Protobufs......Page 561
Loading and Parsing Examples......Page 563
Handling Lists of Lists Using the SequenceExample Protobuf......Page 565
Preprocessing the Input Features......Page 566
Encoding Categorical Features Using One-Hot Vectors......Page 567
Encoding Categorical Features Using Embeddings......Page 570
Keras Preprocessing Layers......Page 576
TF Transform......Page 579
The TensorFlow Datasets (TFDS) Project......Page 581
Exercises......Page 582
14. Deep Computer Vision Using Convolutional Neural Networks......Page 587
The Architecture of the Visual Cortex......Page 588
Convolutional Layers......Page 589
Filters......Page 592
Stacking Multiple Feature Maps......Page 593
TensorFlow Implementation......Page 596
Memory Requirements......Page 600
Pooling Layers......Page 601
TensorFlow Implementation......Page 603
CNN Architectures......Page 606
LeNet-5......Page 609
AlexNet......Page 611
GoogLeNet......Page 615
ResNet......Page 620
Xception......Page 625
SENet......Page 627
Implementing a ResNet-34 CNN Using Keras......Page 630
Using Pretrained Models from Keras......Page 632
Pretrained Models for Transfer Learning......Page 634
Classification and Localization......Page 638
Object Detection......Page 640
Fully Convolutional Networks......Page 642
You Only Look Once (YOLO)......Page 645
Semantic Segmentation......Page 648
Exercises......Page 653
15. Processing Sequences Using RNNs and CNNs......Page 658
Recurrent Neurons and Layers......Page 659
Memory Cells......Page 662
Input and Output Sequences......Page 663
Training RNNs......Page 665
Forecasting a Time Series......Page 666
Baseline Metrics......Page 668
Implementing a Simple RNN......Page 669
Deep RNNs......Page 671
Forecasting Several Time Steps Ahead......Page 673
Fighting the Unstable Gradients Problem......Page 679
Tackling the Short-Term Memory Problem......Page 682
Exercises......Page 692
16. Natural Language Processing with RNNs and Attention......Page 695
Generating Shakespearean Text Using a Character RNN......Page 696
Creating the Training Dataset......Page 697
How to Split a Sequential Dataset......Page 698
Chopping the Sequential Dataset into Multiple Windows......Page 699
Building and Training the Char-RNN Model......Page 702
Using the Char-RNN Model......Page 703
Generating Fake Shakespearean Text......Page 704
Stateful RNN......Page 705
Sentiment Analysis......Page 708
Masking......Page 713
Reusing Pretrained Embeddings......Page 716
An Encoder–Decoder Network for Neural Machine Translation......Page 718
Bidirectional RNNs......Page 722
Beam Search......Page 724
Attention Mechanisms......Page 726
Visual Attention......Page 730
Attention Is All You Need: The Transformer Architecture......Page 732
Recent Innovations in Language Models......Page 745
Exercises......Page 747
17. Representation Learning and Generative Learning Using Autoencoders and GANs......Page 752
Efficient Data Representations......Page 754
Performing PCA with an Undercomplete Linear Autoencoder......Page 756
Stacked Autoencoders......Page 758
Implementing a Stacked Autoencoder Using Keras......Page 759
Visualizing the Reconstructions......Page 760
Visualizing the Fashion MNIST Dataset......Page 762
Unsupervised Pretraining Using Stacked Autoencoders......Page 763
Tying Weights......Page 765
Training One Autoencoder at a Time......Page 766
Convolutional Autoencoders......Page 768
Recurrent Autoencoders......Page 769
Denoising Autoencoders......Page 770
Sparse Autoencoders......Page 772
Variational Autoencoders......Page 776
Generating Fashion MNIST Images......Page 782
Generative Adversarial Networks......Page 784
The Difficulties of Training GANs......Page 789
Deep Convolutional GANs......Page 792
Progressive Growing of GANs......Page 796
StyleGANs......Page 799
Exercises......Page 803
Learning to Optimize Rewards......Page 806
Policy Search......Page 808
Introduction to OpenAI Gym......Page 810
Neural Network Policies......Page 815
Evaluating Actions: The Credit Assignment Problem......Page 817
Policy Gradients......Page 819
Markov Decision Processes......Page 825
Temporal Difference Learning......Page 830
Q-Learning......Page 831
Exploration Policies......Page 833
Approximate Q-Learning and Deep Q-Learning......Page 834
Implementing Deep Q-Learning......Page 835
Fixed Q-Value Targets......Page 841
Double DQN......Page 842
Dueling DQN......Page 843
The TF-Agents Library......Page 844
Installing TF-Agents......Page 846
TF-Agents Environments......Page 847
Environment Specifications......Page 848
Environment Wrappers and Atari Preprocessing......Page 849
Training Architecture......Page 854
Creating the Deep Q-Network......Page 856
Creating the DQN Agent......Page 858
Creating the Replay Buffer and the Corresponding Observer......Page 860
Creating Training Metrics......Page 862
Creating the Collect Driver......Page 863
Creating the Dataset......Page 865
Creating the Training Loop......Page 868
Overview of Some Popular RL Algorithms......Page 870
Exercises......Page 872
19. Training and Deploying TensorFlow Models at Scale......Page 875
Using TensorFlow Serving......Page 876
Creating a Prediction Service on GCP AI Platform......Page 888
Using the Prediction Service......Page 894
Deploying a Model to a Mobile or Embedded Device......Page 898
Using GPUs to Speed Up Computations......Page 904
Getting Your Own GPU......Page 906
Using a GPU-Equipped Virtual Machine......Page 909
Colaboratory......Page 910
Managing the GPU RAM......Page 912
Placing Operations and Variables on Devices......Page 915
Parallel Execution Across Multiple Devices......Page 918
Model Parallelism......Page 922
Data Parallelism......Page 925
Training at Scale Using the Distribution Strategies API......Page 931
Training a Model on a TensorFlow Cluster......Page 933
Running Large Training Jobs on Google Cloud AI Platform......Page 938
Black Box Hyperparameter Tuning on AI Platform......Page 940
Exercises......Page 942
Thank You!......Page 943
Chapter 1: The Machine Learning Landscape......Page 946
Chapter 2: End-to-End Machine Learning Project......Page 949
Chapter 3: Classification......Page 950
Chapter 4: Training Models......Page 951
Chapter 5: Support Vector Machines......Page 954
Chapter 6: Decision Trees......Page 957
Chapter 7: Ensemble Learning and Random Forests......Page 959
Chapter 8: Dimensionality Reduction......Page 961
Chapter 9: Unsupervised Learning Techniques......Page 964
Chapter 10: Introduction to Artificial Neural Networks with Keras......Page 967
Chapter 11: Training Deep Neural Networks......Page 971
Chapter 12: Custom Models and Training with TensorFlow......Page 974
Chapter 13: Loading and Preprocessing Data with TensorFlow......Page 978
Chapter 14: Deep Computer Vision Using Convolutional Neural Networks......Page 983
Chapter 15: Processing Sequences Using RNNs and CNNs......Page 988
Chapter 16: Natural Language Processing with RNNs and Attention......Page 992
Chapter 17: Representation Learning and Generative Learning Using Autoencoders and GANs......Page 995
Chapter 18: Reinforcement Learning......Page 998
Chapter 19: Training and Deploying TensorFlow Models at Scale......Page 1002
Frame the Problem and Look at the Big Picture......Page 1008
Get the Data......Page 1009
Explore the Data......Page 1010
Prepare the Data......Page 1011
Shortlist Promising Models......Page 1012
Fine-Tune the System......Page 1013
Launch!......Page 1014
C. SVM Dual Problem......Page 1016
Manual Differentiation......Page 1020
Finite Difference Approximation......Page 1021
Forward-Mode Autodiff......Page 1022
Reverse-Mode Autodiff......Page 1026
Hopfield Networks......Page 1030
Boltzmann Machines......Page 1032
Restricted Boltzmann Machines......Page 1035
Deep Belief Nets......Page 1036
Self-Organizing Maps......Page 1039
Strings......Page 1042
Ragged Tensors......Page 1044
Sparse Tensors......Page 1045
Tensor Arrays......Page 1046
Sets......Page 1047
Queues......Page 1048
TF Functions and Concrete Functions......Page 1051
Exploring Function Definitions and Graphs......Page 1053
A Closer Look at Tracing......Page 1055
Using AutoGraph to Capture Control Flow......Page 1057
Handling Variables and Other Resources in TF Functions......Page 1059
Using TF Functions with tf.keras (or Not)......Page 1061
Index......Page 1063