Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo
O'Reilly
2018-01-19
|
| Ausgabe: | First edition, fifth release |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xx, 549 Seiten Illustrationen, Diagramme |
| ISBN: | 9781491962299 |
Internformat
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Datensatz im Suchindex
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|---|---|
| adam_text | 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
in
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 56
Experimenting with Attribute Combinations 59
Prepare the Data for Machine Learning Algorithms 60
Data Cleaning 61
Handling Text and Categorical Attributes 63
Custom Transformers 65
Feature Scaling 66
Transformation Pipelines 67
Select and Train a Model 69
Training and Evaluating on the Training Set 69
Better Evaluation Using Cross-Validation 71
Fine-Tune Your Model p 73
Grid Search 73
Randomized Search 75
Ensemble Methods 76
Analyze the Best Models and Their Errors 76
Evaluate Your System on the Test Set 77
Launch, Monitor, and Maintain Your System 78
Try It Out! 78
Exercises 79
3. Classification....................................................... 81
MNIST 81
Training a Binary Classifier 84
Performance Measures 84
Measuring Accuracy Using Cross-Validation 85
Confusion Matrix 86
Precision and Recall 88
Precision/Recall Tradeoff 89
The ROC Curve 93
Multiclass Classification 95
Error Analysis 98
Multilabel Classification 102
Multioutput Classification 103
iv | Table of Contents
Exercises
104
Training Models........................................ ...................107
Linear Regression 108
The Normal Equation 110
Computational Complexity 112
Gradient Descent 113
Batch Gradient Descent 116
Stochastic Gradient Descent 119
Mini-batch Gradient Descent 121
Polynomial Regression 123
Learning Curves 125
Regularized Linear Models 129
Ridge Regression 129
Lasso Regression 132
Elastic Net 134
Early Stopping 135
Logistic Regression 136
Estimating Probabilities 136
Training and Cost Function 137
Decision Boundaries 139
Softmax Regression 141
Exercises 145
Support Vector Machines................................ .................. 147
Linear SVM Classification 147
Soft Margin Classification 148
Nonlinear SVM Classification 151
Polynomial Kernel 152
Adding Similarity Features 153
Gaussian RBF Kernel 154
Computational Complexity 156
SVM Regression 156
Under the Hood 158
Decision Function and Predictions 158
Training Objective 159
Quadratic Programming 161
The Dual Problem 162
Kernelized SVM 163
Online SVMs 166
Exercises 167
Table of Contents | v
6. Decision Trees........................................................ 169
Training and Visualizing a Decision Tree 169
Making Predictions 171
Estimating Class Probabilities 173
The CART Training Algorithm 173
Computational Complexity 174
Gini Impurity or Entropy? 174
Regularization Hyperparameters 175
Regression 177
Instability 179
Exercises 180
7. Ensemble Learning and Random Forests..................................183
Voting Classifiers 183
Bagging and Pasting 187
Bagging and Pasting in Scikit-Learn 188
Out-of-Bag Evaluation 189
Random Patches and Random Subspaces 190
Random Forests 191
Extra-Trees 192
Feature Importance 192
Boosting 193
AdaBoost 194
Gradient Boosting 197
Stacking 202
Exercises 204
8. Dimensionality Reduction.............................................. 207
The Curse of Dimensionality 208
Main Approaches for Dimensionality Reduction 209
Projection 209
Manifold Learning 212
PCA 213
Preserving the Variance 213
Principal Components 214
Projecting Down to d Dimensions 215
Using Scikit-Learn 216
Explained Variance Ratio 216
Choosing the Right Number of Dimensions 217
PCA for Compression 218
Incremental PCA 219
Randomized PCA 220
vi | Table of Contents
Kernel PCA 220
Selecting a Kernel and Tuning Hyperparameters 221
LLE 223
Other Dimensionality Reduction Techniques 225
Exercises 226
Part II. Neural Networks and Deep Learning
9. Up and Running with TensorFlow.........................................231
Installation 234
Creating Your First Graph and Running It in a Session 234
Managing Graphs 236
Lifecycle of a Node Value 237
Linear Regression with TensorFlow 237
Implementing Gradient Descent 239
Manually Computing the Gradients 239
Using autodiff 240
Using an Optimizer 241
Feeding Data to the Training Algorithm 241
Saving and Restoring Models 243
Visualizing the Graph and Training Curves Using TensorBoard 244
Name Scopes 247
Modularity 248
Sharing Variables 250
Exercises 253
10. Introduction to Artificial Neural Networks............................ 255
From Biological to Artificial Neurons 256
Biological Neurons 257
Logical Computations with Neurons 258
The Perceptron 259
Multi-Layer Perceptron and Backpropagation 263
Training an MLP with TensorFlows High-Level API 266
Training a DNN Using Plain TensorFlow 267
Construction Phase 267
Execution Phase 271
Using the Neural Network 272
Fine-Tuning Neural Network Hyperparameters 272
Number of Hidden Layers 273
Number of Neurons per Hidden Layer 274
Activation Functions 274
vii
Table of Contents
Exercises
275
11. Training Deep Neural Nets............................................ 277
Vanishing/Exploding Gradients Problems 277
Xavier and He Initialization 279
Nonsaturating Activation Functions 281
Batch Normalization 284
Gradient Clipping 288
Reusing Pretrained Layers 289
Reusing a TensorFlow Model 289
Reusing Models from Other Frameworks 291
Freezing the Lower Layers 292
Caching the Frozen Layers 293
Tweaking, Dropping, or Replacing the Upper Layers 294
Model Zoos 294
Unsupervised Pretraining 295
Pretraining on an Auxiliary Task 296
Faster Optimizers 297
Momentum Optimization 297
Nesterov Accelerated Gradient 299
AdaGrad 300
RMSProp 302
Adam Optimization 302
Learning Rate Scheduling 305
Avoiding Overfitting Through Regularization 307
Early Stopping 307
Cj and i2 Regularization 307
Dropout 309
Max-Norm Regularization 311
Data Augmentation 313
Practical Guidelines 314
Exercises 315
12. Distributing TensorFlow Across Devices and Servers................... 317
Multiple Devices on a Single Machine 318
Installation 318
Managing the GPU RAM 321
Placing Operations on Devices 322
Parallel Execution 325
Control Dependencies 327
Multiple Devices Across Multiple Servers 328
Opening a Session 330
viii J Table of Contents
The Master and Worker Services 330
Pinning Operations Across Tasks 331
Sharding Variables Across Multiple Parameter Servers 331
Sharing State Across Sessions Using Resource Containers 332
Asynchronous Communication Using TensorFlow Queues 334
Loading Data Directly from the Graph 339
Parallelizing Neural Networks on a TensorFlow Cluster 346
One Neural Network per Device 346
In-Graph Versus Between-Graph Replication 347
Model Parallelism 350
Data Parallelism 352
Exercises 357
13. Convolutional Neural Networks......................................... 359
The Architecture of the Visual Cortex 360
Convolutional Layer 361
Filters 363
Stacking Multiple Feature Maps 364
TensorFlow Implementation 366
Memory Requirements 368
Pooling Layer 369
CNN Architectures 371
LeNet-5 372
AlexNet 373
GoogLeNet 375
ResNet 378
Exercises 382
14. Recurrent Neural Networks..............................................385
Recurrent Neurons 386
Memory Cells 388
Input and Output Sequences 389
Basic RNNs in TensorFlow 390
Static Unrolling Through Time 391
Dynamic Unrolling Through Time 393
Handling Variable Length Input Sequences 394
Handling Variable-Length Output Sequences 395
Training RNNs 395
Training a Sequence Classifier 396
Training to Predict Time Series 398
Creative RNN 402
Deep RNNs 403
Table of Contents | ix
Distributing a Deep RNN Across Multiple GPUs 404
Applying Dropout 405
The Difficulty of Training over Many Time Steps 406
LSTM Cell 407
Peephole Connections 410
GRU Cell 410
Natural Language Processing 412
Word Embeddings 412
An Encoder-Decoder Network for Machine Translation 414
Exercises 417
15. Autoencoders....................................................... 419
Efficient Data Representations 420
Performing PC A with an Undercomplete Linear Autoencoder 421
Stacked Autoencoders 423
TensorFlow Implementation 424
Tying Weights 425
Training One Autoencoder at a Time 426
Visualizing the Reconstructions 429
Visualizing Features 429
Unsupervised Pretraining Using Stacked Autoencoders 430
Denoising Autoencoders 432
TensorFlow Implementation 433
Sparse Autoencoders 434
TensorFlow Implementation 436
Variational Autoencoders 437
Generating Digits 440
Other Autoencoders 441
Exercises 442
16. Reinforcement Learning.............................................. 445
Learning to Optimize Rewards 446
Policy Search 448
Introduction to OpenAI Gym 449
Neural Network Policies 453
Evaluating Actions: The Credit Assignment Problem 455
Policy Gradients 45 6
Markov Decision Processes 461
Temporal Difference Learning and Q-Learning 465
Exploration Policies 467
Approximate Q-Learning and Deep Q-Learning 468
Learning to Play Ms. Pac-Man Using the DQN Algorithm 469
x | Table of Contents
Exercises 477
Thank You! 478
A. Exercise Solutions............................................................ 479
B. Machine Learning Project Checklist.............................................505
C. SVM Dual Problem.............................................................. 511
D. Autodiff...................................................................... 515
E. Other Popular ANN Architectures................................................523
Index............................................................................ 533
Table of Contents | xi
|
| any_adam_object | 1 |
| author | Géron, Aurélien |
| author_GND | (DE-588)1131560930 |
| author_facet | Géron, Aurélien |
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| discipline | Informatik |
| edition | First edition, fifth release |
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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, fifth release Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo O'Reilly 2018-01-19 © 2017 xx, 549 Seiten Illustrationen, Diagramme txt rdacontent sti rdacontent n rdamedia nc rdacarrier Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Programmbibliothek (DE-588)4121521-7 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf 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 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030320590&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 Python Programmiersprache (DE-588)4434275-5 gnd Programmbibliothek (DE-588)4121521-7 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4434275-5 (DE-588)4121521-7 (DE-588)4033447-8 (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 | Python Programmiersprache (DE-588)4434275-5 gnd Programmbibliothek (DE-588)4121521-7 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Python Programmiersprache Programmbibliothek Künstliche Intelligenz Maschinelles Lernen |
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