更新时间：2021-06-25 22:08:15
封面
版权信息
Dedication
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Contributors
About the author
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Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
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Conventions used
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Reviews
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
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
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
