Mastering Machine Learning Algorithms电子书

Title Page

Copyright and Credits

Mastering Machine Learning Algorithms

Dedication

Packt Upsell

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Contributors

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Preface

Who this book is for

What this book covers

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Conventions used

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Machine Learning Model Fundamentals

Models and data

Zero-centering and whitening

Training and validation sets

Cross-validation

Features of a machine learning model

Capacity of a model

Vapnik-Chervonenkis capacity

Bias of an estimator

Underfitting

Variance of an estimator

Overfitting

The Cramér-Rao bound

Loss and cost functions

Examples of cost functions

Mean squared error

Huber cost function

Hinge cost function

Categorical cross-entropy

Regularization

Ridge

Lasso

ElasticNet

Early stopping

Summary

Introduction to Semi-Supervised Learning

Semi-supervised scenario

Transductive learning

Inductive learning

Semi-supervised assumptions

Smoothness assumption

Cluster assumption

Manifold assumption

Generative Gaussian mixtures

Example of a generative Gaussian mixture

Weighted log-likelihood

Contrastive pessimistic likelihood estimation

Example of contrastive pessimistic likelihood estimation

Semi-supervised Support Vector Machines (S3VM)

Example of S3VM

Transductive Support Vector Machines (TSVM)

Example of TSVM

Summary

Graph-Based Semi-Supervised Learning

Label propagation

Example of label propagation

Label propagation in Scikit-Learn

Label spreading

Example of label spreading

Label propagation based on Markov random walks

Example of label propagation based on Markov random walks

Manifold learning

Isomap

Example of Isomap

Locally linear embedding

Example of locally linear embedding

Laplacian Spectral Embedding

Example of Laplacian Spectral Embedding

t-SNE

Example of t-distributed stochastic neighbor embedding

Summary

Bayesian Networks and Hidden Markov Models

Conditional probabilities and Bayes' theorem

Bayesian networks

Sampling from a Bayesian network

Direct sampling

Example of direct sampling

A gentle introduction to Markov chains

Gibbs sampling

Metropolis-Hastings sampling

Example of Metropolis-Hastings sampling

Sampling example using PyMC3

Hidden Markov Models (HMMs)

Forward-backward algorithm

Forward phase

Backward phase

HMM parameter estimation

Example of HMM training with hmmlearn

Viterbi algorithm

Finding the most likely hidden state sequence with hmmlearn

Summary

EM Algorithm and Applications

MLE and MAP learning

EM algorithm

An example of parameter estimation

Gaussian mixture

An example of Gaussian Mixtures using Scikit-Learn

Factor analysis

An example of factor analysis with Scikit-Learn

Principal Component Analysis

An example of PCA with Scikit-Learn

Independent component analysis

An example of FastICA with Scikit-Learn

Addendum to HMMs

Summary

Hebbian Learning and Self-Organizing Maps

Hebb's rule

Analysis of the covariance rule

Example of covariance rule application

Weight vector stabilization and Oja's rule

Sanger's network

Example of Sanger's network

Rubner-Tavan's network

Example of Rubner-Tavan's network

Self-organizing maps

Example of SOM

Summary

Clustering Algorithms

k-Nearest Neighbors

KD Trees

Ball Trees

Example of KNN with Scikit-Learn

K-means

K-means++

Example of K-means with Scikit-Learn

Evaluation metrics

Homogeneity score

Completeness score

Adjusted Rand Index

Silhouette score

Fuzzy C-means

Example of fuzzy C-means with Scikit-Fuzzy

Spectral clustering

Example of spectral clustering with Scikit-Learn

Summary

Ensemble Learning

Ensemble learning fundamentals

Random forests

Example of random forest with Scikit-Learn

AdaBoost

AdaBoost.SAMME

AdaBoost.SAMME.R

AdaBoost.R2

Example of AdaBoost with Scikit-Learn

Gradient boosting

Example of gradient tree boosting with Scikit-Learn

Ensembles of voting classifiers

Example of voting classifiers with Scikit-Learn

Ensemble learning as model selection

Summary

Neural Networks for Machine Learning

The basic artificial neuron

Perceptron

Example of a perceptron with Scikit-Learn

Multilayer perceptrons

Activation functions

Sigmoid and hyperbolic tangent

Rectifier activation functions

Softmax

Back-propagation algorithm

Stochastic gradient descent

Weight initialization

Example of MLP with Keras

Optimization algorithms

Gradient perturbation

Momentum and Nesterov momentum

SGD with momentum in Keras

RMSProp

RMSProp with Keras

Adam

Adam with Keras

AdaGrad

AdaGrad with Keras

AdaDelta

AdaDelta with Keras

Regularization and dropout

Dropout

Example of dropout with Keras

Batch normalization

Example of batch normalization with Keras

Summary

Advanced Neural Models

Deep convolutional networks

Convolutions

Bidimensional discrete convolutions

Strides and padding

Atrous convolution

Separable convolution

Transpose convolution

Pooling layers

Other useful layers

Examples of deep convolutional networks with Keras

Example of a deep convolutional network with Keras and data augmentation

Recurrent networks

Backpropagation through time (BPTT)

LSTM

GRU

Example of an LSTM network with Keras

Transfer learning

Summary

Autoencoders

Autoencoders

An example of a deep convolutional autoencoder with TensorFlow

Denoising autoencoders

An example of a denoising autoencoder with TensorFlow

Sparse autoencoders

Adding sparseness to the Fashion MNIST deep convolutional autoencoder

Variational autoencoders

An example of a variational autoencoder with TensorFlow

Summary

Generative Adversarial Networks

Adversarial training

Example of DCGAN with TensorFlow

Wasserstein GAN (WGAN)

Example of WGAN with TensorFlow

Summary

Deep Belief Networks

MRF

RBMs

DBNs

Example of unsupervised DBN in Python

Example of Supervised DBN with Python

Summary

Introduction to Reinforcement Learning

Reinforcement Learning fundamentals

Environment

Rewards

Checkerboard environment in Python

Policy

Policy iteration

Policy iteration in the checkerboard environment

Value iteration

Value iteration in the checkerboard environment

TD(0) algorithm

TD(0) in the checkerboard environment

Summary

Advanced Policy Estimation Algorithms

TD(λ) algorithm

TD(λ) in a more complex Checkerboard environment

Actor-Critic TD(0) in the checkerboard environment

SARSA algorithm

SARSA in the checkerboard environment

Q-learning

Q-learning in the checkerboard environment

Q-learning using a neural network

Summary

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