Hands-On Machine Learning with Scikit-Learn and TensorFlow

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This book assumes that you know close to nothing about Machine Learning. Its goal is to give you the concepts, the intuitions, and the tools you need to actually implement programs capable of learning from data. We will cover a large number of techniques, from the simplest and most commonly used (s
Hands-On Machine Learning with Scikit-Learn and tensorflow Concepts, Tools, and Techniques to Build intelligent Systems Aurelien geron Beijing. Boston. Farnham. Sebastopol. Tokyo OREILLY Hands-On Machine Learning with Scikit-Learn and Tensor Flow y Aurelien geron Copyright O 2017 Aurelien Geron. All rights reserved Printed in the united states of america Published by o reilly media, InC, 1005 Gravenstein Highway North, Sebastopol, CA95472 OReilly books may be purchased for educational, business, or sales promotional use Online editions are alsoavailableformosttitles(http://oreilly.com/safari).Formoreinformationcontactourcorporate/insti tutionalsalesdepartment:800-998-9938orcorporate@oreilly.com Editor: Nicole Tache Indexer: Wendy catalano Production editor: Nicholas adams Interior Designer: David Futato Copyeditor: Rachel Monaghan Cover Designer: Randy Comer Proofreader: Charles roumeliotis Illustrator rebecca demarest March 2017 First edition Revision History for the First Edition 2017-03-10 First Release Seehttp://oreilly.com/catalog/errata.csp?isbn=9781491962299forreleasedetails The O Reilly logo is a registered trademark of O Reilly Media, InC. Hands-On Machine Learning with Scikit-Learn and Tensor Flow, the cover image, and related trade dress are trademarks of O Reilly Media, nc While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights 978-1-49196229-9 Table of contents Preface XIl Part L. The fundamentals of Machine Learning 1. The Machine Learning landscape.................. What Is Machine learning Why Use Machine Learning Types of Machine Learning Systems Supervised/Unsupervised Learning Batch and Online Learning 14 Instance-Based Versus Model-Based Learning 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 2 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 Check the assumptions Get the data Create the Workspace Download the data Take a Quick look at the Data Structure Create a Test set 49 Discover and visualize the data to gain insights 5 Visualizing Geographical Data 53 ooking for Correlations 55 Experimenting with Attribute Combinations 58 Prepare the Data for Machine Learning Algorithms Data Cleaning Handling Text and Categorical Attributes 902 Custom Transformers 64 Feature scaling Transformation Pipelines 66 Select and train a model 68 Training and Evaluating on the Training Set 68 Better Evaluation Using Cross-Validation Fine-Tune your model 71 Grid Search Randomized search 74 Ensemble methods 74 Analyze the best Models and Their errors 74 Evaluate Your System on the Test Set Launch, Monitor, and maintain Your System Try It out! Exercises 77 3. Classification MNIST 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 Multilabel Classification 100 Multioutput Classification 101 iv Table of Contents Exercises 102 4. 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 ogistic Regression 134 Estimating Probabilities 134 Training and Cost Function 135 Decision boundaries 136 R 139 Exercises 42 5. 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 SVMRegression 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 Gini impurity or entropy? 172 Regularization hyperparameters 173 Regression 175 Instabil 177 Exercises 178 7. Ensemble learning and random forests...................181 Voting Classifiers 181 ging and Pasting 185 Bagging and Pasting in Scikit-Learn 186 Out-of-Bag Evaluation 187 Random Patches and Random Subspaces 188 Ra Forests 189 Extra-TI 190 Feature Importance 190 Boosting g 191 Adaboost 192 Gradient boosting 195 200 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 P al 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 Table of contents Kernel pca 218 Selecting a Kernel and Tuning Hyperparameters 219 LLE 221 Other Dimensionality Reduction Techniques 223 Exercises 224 Part I. Neural Networks and Deep Learning 9. Up and Running with TensorFlow 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 Tensor Flow 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 le Percep 257 Multi-layer Perceptron and backpropagation 261 Training an MLP with Tensor Flow's High-Level API 264 Training a DNN USing Plain TensorFlow 26 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|ⅶi 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 pping 286 Reusing pretrained layers 286 Reusing a Tensor Flow 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 290 291 Unsupervised Pretraining 291 Pretraining on an Auxiliary Task 292 Faster Opti 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 e, and e2 Regularization 303 Dropout 304 Max-Norm Regularization 307 Data Augmentation 309 Practical guidelines 310 Exercises 311 12. Distributing tensor Flow 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 I Table of Contents

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