Name: Mastering Machine Learning Algorithms
Author: Giuseppe Bonaccorso
ISBN: 9781788625906

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Mastering Machine Learning Algorithms

Giuseppe Bonaccorso

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Dedication
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Contributors
About the author
About the reviewer
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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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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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Mastering Machine Learning Algorithms