[Packt Publishing] Advanced Machine Learning with Python

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[Packt Publishing] Advanced Machine Learning with Python

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Table of Contents
Preface v
Chapter 1: Unsupervised Machine Learning 1
Principal component analysis 2
PCA – a primer 2
Employing PCA 4
Introducing k-means clustering 7
Clustering – a primer 8
Kick-starting clustering analysis 8
Tuning your clustering configurations 13
Self-organizing maps 18
SOM – a primer 18
Employing SOM 20
Further reading 24
Summary 25
Chapter 2: Deep Belief Networks 27
Neural networks – a primer 28
The composition of a neural network 28
Network topologies 29
Restricted Boltzmann Machine 33
Introducing the RBM 33
Topology 34
Training 35
Applications of the RBM 37
Further applications of the RBM 49
Deep belief networks 49
Training a DBN 50
Applying the DBN 50
Validating the DBN 54
Table of Contents
[ ii ]
Further reading 55
Summary 56
Chapter 3: Stacked Denoising Autoencoders 57
Autoencoders 57
Introducing the autoencoder 58
Topology 58
Training 59
Denoising autoencoders 60
Applying a dA 62
Stacked Denoising Autoencoders 66
Applying the SdA 67
Assessing SdA performance 74
Further reading 75
Summary 75
Chapter 4: Convolutional Neural Networks 77
Introducing the CNN 77
Understanding the convnet topology 79
Understanding convolution layers 81
Understanding pooling layers 85
Training a convnet 88
Putting it all together 88
Applying a CNN 92
Further Reading 99
Summary 100
Chapter 5: Semi-Supervised Learning 101
Introduction 101
Understanding semi-supervised learning 102
Semi-supervised algorithms in action 103
Self-training 103
Implementing self-training 105
Finessing your self-training implementation 110
Contrastive Pessimistic Likelihood Estimation 114
Further reading 126
Summary 127
Chapter 6: Text Feature Engineering 129
Introduction 129
Text feature engineering 130
Cleaning text data 131
Text cleaning with BeautifulSoup 131
Managing punctuation and tokenizing 132
Tagging and categorising words 136
Table of Contents
[ iii ]
Creating features from text data 141
Stemming 141
Bagging and random forests 143
Testing our prepared data 146
Further reading 153
Summary 154
Chapter 7: Feature Engineering Part II 155
Introduction 155
Creating a feature set 156
Engineering features for ML applications 157
Using rescaling techniques to improve the learnability of features 157
Creating effective derived variables 160
Reinterpreting non-numeric features 162
Using feature selection techniques 165
Performing feature selection 167
Feature engineering in practice 175
Acquiring data via RESTful APIs 176
Testing the performance of our model 177
Twitter 180
Deriving and selecting variables using feature engineering techniques 187
Further reading 199
Summary 200
Chapter 8: Ensemble Methods 201
Introducing ensembles 202
Understanding averaging ensembles 203
Using bagging algorithms 203
Using random forests 205
Applying boosting methods 209
Using XGBoost 212
Using stacking ensembles 215
Applying ensembles in practice 218
Using models in dynamic applications 221
Understanding model robustness 222
Identifying modeling risk factors 228
Strategies to managing model robustness 230
Further reading 233
Summary 234
Chapter 9: Additional Python Machine Learning Tools 235
Alternative development tools 236
Introduction to Lasagne 236
Getting to know Lasagne 236
Table of Contents
[ iv ]
Introduction to TensorFlow 239
Getting to know TensorFlow 239
Using TensorFlow to iteratively improve our models 241
Knowing when to use these libraries 244
Further reading 245
Summary 245
Appendix: Chapter Code Requirements 249
Index 251

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关键词：Publishing Advanced Learning Publish machine network reading

学习一下

qingxunz 发表于 2017-6-2 10:48
Advanced Machine Learning with Python

感谢

学习一下，谢谢