Book Image

Mastering Machine Learning Algorithms - Second Edition

By : Giuseppe Bonaccorso

Book Image

Mastering Machine Learning Algorithms - Second Edition

By: Giuseppe Bonaccorso

Overview of this book

Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.

Preface

Who this book is for

What this book covers

To get the most out of this book

Machine Learning Model Fundamentals

Machine Learning Model Fundamentals

Models and data

Characteristics of a machine learning model

Further reading

Free Chapter

Loss Functions and Regularization

Loss Functions and Regularization

Defining loss and cost functions

Further reading

Introduction to Semi-Supervised Learning

Introduction to Semi-Supervised Learning

Semi-supervised scenario

Generative Gaussian Mixture

Further reading

Advanced Semi-Supervised Classification

Advanced Semi-Supervised Classification

Contrastive Pessimistic Likelihood Estimation

Semi-supervised Support Vector Machines (S3VM)

Transductive Support Vector Machines (TSVM)

Further reading

Graph-Based Semi-Supervised Learning

Graph-Based Semi-Supervised Learning

Label propagation

Example of label propagation

Label spreading

Label propagation based on Markov random walks

Manifold learning

Further reading

Clustering and Unsupervised Models

Clustering and Unsupervised Models

K-nearest neighbors

Evaluation metrics

Further reading

Advanced Clustering and Unsupervised Models

Advanced Clustering and Unsupervised Models

Spectral clustering

Further reading

Clustering and Unsupervised Models for Marketing

Clustering and Unsupervised Models for Marketing

Introduction to Market Basket Analysis with the Apriori Algorithm

Further reading

Generalized Linear Models and Regression

Generalized Linear Models and Regression

Other regression techniques

Further reading

Introduction to Time-Series Analysis

Introduction to Time-Series Analysis

Introduction to linear models for time-series

Further reading

Bayesian Networks and Hidden Markov Models

Bayesian Networks and Hidden Markov Models

Conditional probabilities and Bayes' theorem

Bayesian networks

Hidden Markov Models

Further reading

The EM Algorithm

The EM Algorithm

MLE and MAP Learning

Gaussian Mixture

Further reading

Component Analysis and Dimensionality Reduction

Component Analysis and Dimensionality Reduction

Factor Analysis

Principal Component Analysis

Independent Component Analysis

Addendum to Hidden Markov Models

Further reading

Hebbian Learning

Hebbian Learning

Sanger's network

Rubner-Tavan's network

Self-organizing maps

Further reading

Fundamentals of Ensemble Learning

Fundamentals of Ensemble Learning

Ensemble learning fundamentals

Further reading

Advanced Boosting Algorithms

Advanced Boosting Algorithms

Gradient boosting

Ensembles of voting classifiers

Ensemble learning as model selection

Further reading

Modeling Neural Networks

Modeling Neural Networks

The basic artificial neuron

Multilayer Perceptrons (MLPs)

The back-propagation algorithm

Further reading

Optimizing Neural Networks

Optimizing Neural Networks

Optimization algorithms

Regularization and Dropout

Batch normalization

Further reading

Deep Convolutional Networks

Deep Convolutional Networks

Deep convolutional networks

Convolutional operators

Example of a deep convolutional network with TensorFlow and Keras

Further reading

Recurrent Neural Networks

Recurrent Neural Networks

Recurrent networks

Long Short-Term Memory (LSTM)

Transfer learning

Further reading

Autoencoders

Denoising autoencoders

Sparse autoencoders

Variational autoencoders

Further reading

Introduction to Generative Adversarial Networks

Introduction to Generative Adversarial Networks

Adversarial training

Deep Convolutional GANs

Wasserstein GAN

Further reading

Deep Belief Networks

Deep Belief Networks

Introduction to Markov random fields

Restricted Boltzmann Machines

Deep Belief Networks

Further reading

Introduction to Reinforcement Learning

Introduction to Reinforcement Learning

Fundamental concepts of RL

Policy iteration

Value iteration

The TD(0) algorithm

Further reading

Advanced Policy Estimation Algorithms

Advanced Policy Estimation Algorithms

TD(λ) algorithm

SARSA algorithm

Direct policy search through policy gradient

Further reading

Other Books You May Enjoy

Other Books You May Enjoy

Index

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Summary

In this chapter, we analyzed three different approaches to component extraction. FA assumes that we have a small number of Gaussian latent variables and a Gaussian decorrelated noise term. The only restriction on the noise is to have a diagonal covariance matrix, so two different scenarios are possible. When we are in the presence of heteroscedastic noise, the process is an actual FA. When, instead, the noise is homoscedastic, the algorithm becomes the equivalent of a PCA. In this case, the process is equivalent to check the sample space in order to find the directions where the variance is higher. Selecting only the most important directions, we can project the original dataset onto a low-dimensional subspace, where the covariance matrix becomes decorrelated.

One of the problems of both FA and PCA is their assumption to model the latent variables with Gaussian distributions. This choice simplifies the model but, at the same time, yields dense representations where...