Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems
Gespeichert in:
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo
O'Reilly Media
March 2017
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| Ausgabe: | First edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | xx, 551 Seiten Illustrationen, Diagramme |
| ISBN: | 9781491962299 |
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| adam_text | Hands-On Machine Learning with
Scikit-Learn and TensorFlow
Concepts, Tools, and Techniques to
Build Intelligent Systems
Aurelien Geron
Beijinc
ULB Darmstadt
• Tokyo
O’REILLY
19697070
Table of Contents
Preface xiii
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 7
Supervised/Unsupervised Learning 8
Batch and Online Learning 14
Instance-Based Versus Model-Based Learning 17
Main Challenges of Machine Learning 22
Insufficient Quantity of Training Data 22
Nonrepresentative Training Data 24
Poor-Quality Data 25
Irrelevant Features 25
Overfitting the Training Data 26
Underfitting the Training Data 28
Stepping Back 28
Testing and Validating 29
Exercises 31
2 End-to-End Machine Learning Project 33
Working with Real Data 33
Look at the Big Picture 35
Frame the Problem 35
Select a Performance Measure 37
iii
Check the Assumptions 40
Get the Data 40
Create the Workspace 40
Download the Data 43
Take a Quick Look at the Data Structure 45
Create a Test Set 49
Discover and Visualize the Data to Gain Insights 53
Visualizing Geographical Data 53
Looking for Correlations 55
Experimenting with Attribute Combinations 58
Prepare the Data for Machine Learning Algorithms 59
Data Cleaning 60
Handling Text and Categorical Attributes 62
Custom Transformers 64
Feature Scaling 65
Transformation Pipelines 66
Select and Train a Model 68
Training and Evaluating on the Training Set 68
Better Evaluation Using Cross-Validation 69
Fine-Tune Your Model 71
Grid Search 72
Randomized Search 74
Ensemble Methods 74
Analyze the Best Models and Their Errors 74
Evaluate Your System on the Test Set 75
Launch, Monitor, and Maintain Your System 76
Try It Out! 77
Exercises 77
3 Classification 79
MNIST 79
Training a Binary Classifier 82
Performance Measures 82
Measuring Accuracy Using Cross-Validation 83
Confusion Matrix 84
Precision and Recall 86
Precision/Recall Tradeoff 87
The ROC Curve 91
Multiclass Classification 93
Error Analysis 96
Multilabel Classification 100
Multioutput Classification 101
iv | Table of Contents
Exercises
102
Training Models 105
Linear Regression 106
The Normal Equation 108
Computational Complexity 110
Gradient Descent 111
Batch Gradient Descent 114
Stochastic Gradient Descent 117
Mini-batch Gradient Descent 119
Polynomial Regression 121
Learning Curves 123
Regularized Linear Models 127
Ridge Regression 127
Lasso Regression 130
Elastic Net 132
Early Stopping 133
Logistic Regression 134
Estimating Probabilities 134
Training and Cost Function 135
Decision Boundaries 136
Softmax Regression 139
Exercises 142
Support Vector Machines 145
Linear SVM Classification 145
Soft Margin Classification 146
Nonlinear SVM Classification 149
Polynomial Kernel 150
Adding Similarity Features 151
Gaussian RBF Kernel 152
Computational Complexity 153
SVM Regression 154
Under the Hood 156
Decision Function and Predictions 156
Training Objective 157
Quadratic Programming 159
The Dual Problem 160
Kernelized SVM 161
Online SVMs 164
Exercises 165
Table of Contents | v
6 Decision Trees 167
Training and Visualizing a Decision Tree 167
Making Predictions 169
Estimating Class Probabilities 171
The CART Training Algorithm 171
Computational Complexity 172
Gini Impurity or Entropy? 172
Regularization Hyperparameters 173
Regression 175
Instability 177
Exercises 178
7 Ensemble Learning and Random Forests 181
Voting Classifiers 181
Bagging and Pasting 185
Bagging and Pasting in Scikit-Learn 186
Out-of-Bag Evaluation 187
Random Patches and Random Subspaces 188
Random Forests 189
Extra-Trees 190
Feature Importance 190
Boosting 191
AdaBoost 192
Gradient Boosting 195
Stacking 200
Exercises 202
8 Dimensionality Reduction 205
The Curse of Dimensionality 206
Main Approaches for Dimensionality Reduction 207
Projection 207
Manifold Learning 210
PCA 211
Preserving the Variance 211
Principal Components 212
Projecting Down to d Dimensions 213
Using Scikit-Learn 214
Explained Variance Ratio 214
Choosing the Right Number of Dimensions 215
PCA for Compression 216
Incremental PCA 217
Randomized PCA 218
vi | Table of Contents
Kernel PCA 218
Selecting a Kernel and Tuning Hyperparameters 219
LLE 221
Other Dimensionality Reduction Techniques 223
Exercises 224
Part II Neural Networks and Deep Learning
9 Up and Running with TensorFlow 229
Installation 232
Creating Your First Graph and Running It in a Session 232
Managing Graphs 234
Lifecycle of a Node Value 235
Linear Regression with TensorFlow 235
Implementing Gradient Descent 237
Manually Computing the Gradients 237
Using autodiff 238
Using an Optimizer 239
Feeding Data to the Training Algorithm 239
Saving and Restoring Models 241
Visualizing the Graph and Training Curves Using TensorBoard 242
Name Scopes 245
Modularity 246
Sharing Variables 248
Exercises 251
10 Introduction to Artificial Neural Networks 253
From Biological to Artificial Neurons 254
Biological Neurons 255
Logical Computations with Neurons 256
The Perceptron 257
Multi-Layer Perceptron and Backpropagation 261
Training an MLP with TensorFlow s High-Level API 264
Training a DNN Using Plain TensorFlow 265
Construction Phase 265
Execution Phase 269
Using the Neural Network 270
Fine-Tuning Neural Network Hyperparameters 270
Number of Hidden Layers 270
Number of Neurons per Hidden Layer 272
Activation Functions 272
Table of Contents | vii
Exercises
273
11 Training Deep Neural Nets 275
Vanishing/Exploding Gradients Problems 275
Xavier and He Initialization 277
Nonsaturating Activation Functions 279
Batch Normalization 282
Gradient Clipping 286
Reusing Pretrained Layers 286
Reusing a TensorFlow Model 287
Reusing Models from Other Frameworks 288
Freezing the Lower Layers 289
Caching the Frozen Layers 290
Tweaking, Dropping, or Replacing the Upper Layers 290
Model Zoos 291
Unsupervised Pretraining 291
Pretraining on an Auxiliary Task 292
Faster Optimizers 293
Momentum optimization 294
Nesterov Accelerated Gradient 295
AdaGrad 296
RMSProp 298
Adam Optimization 298
Learning Rate Scheduling 300
Avoiding Overfitting Through Regularization 302
Early Stopping 303
and i2 Regularization 303
Dropout 304
Max-Norm Regularization 307
Data Augmentation 309
Practical Guidelines 310
Exercises 311
12 Distributing TensorFlow Across Devices and Servers 313
Multiple Devices on a Single Machine 314
Installation 314
Managing the GPU RAM 317
Placing Operations on Devices 318
Parallel Execution 321
Control Dependencies 323
Multiple Devices Across Multiple Servers 323
Opening a Session 325
viii | Table of Contents
The Master and Worker Services 325
Pinning Operations Across Tasks 326
Sharding Variables Across Multiple Parameter Servers 327
Sharing State Across Sessions Using Resource Containers 328
Asynchronous Communication Using TensorFlow Queues 329
Loading Data Directly from the Graph 335
Parallelizing Neural Networks on a TensorFlow Cluster 342
One Neural Network per Device 342
In-Graph Versus Between-Graph Replication 343
Model Parallelism 345
Data Parallelism 347
Exercises 352
13 Convolutional Neural Networks 353
The Architecture of the Visual Cortex 354
Convolutional Layer 355
Filters 357
Stacking Multiple Feature Maps 358
TensorFlow Implementation 360
Memory Requirements 362
Pooling Layer 363
CNN Architectures 365
LeNet-5 366
AlexNet 367
GoogLeNet 369
ResNet 372
Exercises 376
14 Recurrent Neural Networks 379
Recurrent Neurons 380
Memory Cells 382
Input and Output Sequences 382
Basic RNNs in TensorFlow 384
Static Unrolling Through Time 385
Dynamic Unrolling Through Time 387
Handling Variable Length Input Sequences 387
Handling Variable-Length Output Sequences 388
Training RNNs 389
Training a Sequence Classifier 389
Training to Predict Time Series 392
Creative RNN 396
Deep RNNs 396
Table of Contents | ix
Distributing a Deep RNN Across Multiple GPUs 397
Applying Dropout 399
The Difficulty of Training over Many Time Steps 400
LSTM Cell 401
Peephole Connections 403
GRU Cell 404
Natural Language Processing 405
Word Embeddings 405
An Encoder-Decoder Network for Machine Translation 407
Exercises 410
15 Autoencoders 413
Efficient Data Representations 414
Performing PCA with an Undercomplete Linear Autoencoder 415
Stacked Autoencoders 417
TensorFlow Implementation 418
Tying Weights 419
Training One Autoencoder at a Time 420
Visualizing the Reconstructions 422
Visualizing Features 423
Unsupervised Pretraining Using Stacked Autoencoders 424
Denoising Autoencoders 426
TensorFlow Implementation 427
Sparse Autoencoders 428
TensorFlow Implementation 429
Variational Autoencoders 430
Generating Digits 433
Other Autoencoders 434
Exercises 435
16 Reinforcement Learning 439
Learning to Optimize Rewards 440
Policy Search 442
Introduction to OpenAI Gym 443
Neural Network Policies 446
Evaluating Actions: The Credit Assignment Problem 449
Policy Gradients 450
Markov Decision Processes 455
Temporal Difference Learning and Q-Learning 459
Exploration Policies 461
Approximate Q-Learning 462
Learning to Play Ms Pac-Man Using Deep Q-Learning 462
x | Table of Contents
Exercises 471
Thank You! 472
A Exercise Solutions 473
B Machine Learning Project Checklist 499
C SVM Dual Problem 505
D Autodiff 509
E Other Popular ANN Architectures 517
Index 527
|
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| spelling | Géron, Aurélien Verfasser (DE-588)1131560930 aut Hands-on machine learning with Scikit-Learn and TensorFlow concepts, tools, and techniques to build intelligent systems Aurélien Géron Hands-on machine learning with Scikit-Learn & TensorFlow First edition Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo O'Reilly Media March 2017 xx, 551 Seiten Illustrationen, Diagramme txt rdacontent sti rdacontent n rdamedia nc rdacarrier Hier auch später erschienene, unveränderte Nachdrucke Künstliche Intelligenz Programmbibliothek (DE-588)4121521-7 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Machine learning Artificial intelligence Künstliche Intelligenz (DE-588)4033447-8 s Maschinelles Lernen (DE-588)4193754-5 s Programmbibliothek (DE-588)4121521-7 s Python Programmiersprache (DE-588)4434275-5 s DE-604 HEBIS Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029692970&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Géron, Aurélien Hands-on machine learning with Scikit-Learn and TensorFlow concepts, tools, and techniques to build intelligent systems Künstliche Intelligenz Programmbibliothek (DE-588)4121521-7 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Python Programmiersprache (DE-588)4434275-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4121521-7 (DE-588)4033447-8 (DE-588)4434275-5 (DE-588)4193754-5 |
| title | Hands-on machine learning with Scikit-Learn and TensorFlow concepts, tools, and techniques to build intelligent systems |
| title_alt | Hands-on machine learning with Scikit-Learn & TensorFlow |
| title_auth | Hands-on machine learning with Scikit-Learn and TensorFlow concepts, tools, and techniques to build intelligent systems |
| title_exact_search | Hands-on machine learning with Scikit-Learn and TensorFlow concepts, tools, and techniques to build intelligent systems |
| title_full | Hands-on machine learning with Scikit-Learn and TensorFlow concepts, tools, and techniques to build intelligent systems Aurélien Géron |
| title_fullStr | Hands-on machine learning with Scikit-Learn and TensorFlow concepts, tools, and techniques to build intelligent systems Aurélien Géron |
| title_full_unstemmed | Hands-on machine learning with Scikit-Learn and TensorFlow concepts, tools, and techniques to build intelligent systems Aurélien Géron |
| title_short | Hands-on machine learning with Scikit-Learn and TensorFlow |
| title_sort | hands on machine learning with scikit learn and tensorflow concepts tools and techniques to build intelligent systems |
| title_sub | concepts, tools, and techniques to build intelligent systems |
| topic | Künstliche Intelligenz Programmbibliothek (DE-588)4121521-7 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Python Programmiersprache (DE-588)4434275-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Künstliche Intelligenz Programmbibliothek Python Programmiersprache Maschinelles Lernen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029692970&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT geronaurelien handsonmachinelearningwithscikitlearnandtensorflowconceptstoolsandtechniquestobuildintelligentsystems AT geronaurelien handsonmachinelearningwithscikitlearntensorflow |