Sign In Start Free Trial

Mastering Machine Learning Algorithms

3.4 (5)

Mastering Machine Learning Algorithms

3.4 (5)

Overview of this book

Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn v0.19.1. You will also learn how to use Keras and TensorFlow 1.x to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need.

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Free Chapter

Machine Learning Model Fundamentals

Machine Learning Model Fundamentals

Models and data

Features of a machine learning model

Loss and cost functions

Summary

Introduction to Semi-Supervised Learning

Introduction to Semi-Supervised Learning

Semi-supervised scenario

Generative Gaussian mixtures

Contrastive pessimistic likelihood estimation

Semi-supervised Support Vector Machines (S3VM)

Transductive Support Vector Machines (TSVM)

Summary

Graph-Based Semi-Supervised Learning

Graph-Based Semi-Supervised Learning

Label propagation

Label spreading

Label propagation based on Markov random walks

Manifold learning

Summary

Bayesian Networks and Hidden Markov Models

Bayesian Networks and Hidden Markov Models

Conditional probabilities and Bayes' theorem

Bayesian networks

Hidden Markov Models (HMMs)

Summary

EM Algorithm and Applications

EM Algorithm and Applications

MLE and MAP learning

EM algorithm

Gaussian mixture

Factor analysis

Principal Component Analysis

Independent component analysis

Addendum to HMMs

Summary

Hebbian Learning and Self-Organizing Maps

Hebbian Learning and Self-Organizing Maps

Hebb's rule

Sanger's network

Rubner-Tavan's network

Self-organizing maps

Summary

Clustering Algorithms

Clustering Algorithms

k-Nearest Neighbors

K-means

Fuzzy C-means

Spectral clustering

Summary

Ensemble Learning

Ensemble Learning

Ensemble learning fundamentals

Random forests

AdaBoost

Gradient boosting

Ensembles of voting classifiers

Ensemble learning as model selection

Summary

Neural Networks for Machine Learning

Neural Networks for Machine Learning

The basic artificial neuron

Perceptron

Multilayer perceptrons

Optimization algorithms

Regularization and dropout

Batch normalization

Summary

Advanced Neural Models

Advanced Neural Models

Deep convolutional networks

Recurrent networks

Transfer learning

Summary

Autoencoders

Autoencoders

Autoencoders

Variational autoencoders

Summary

Generative Adversarial Networks

Generative Adversarial Networks

Adversarial training

Wasserstein GAN (WGAN)

Summary

Deep Belief Networks

Deep Belief Networks

MRF

RBMs

DBNs

Summary

Introduction to Reinforcement Learning

Introduction to Reinforcement Learning

Reinforcement Learning fundamentals

Policy iteration

Value iteration

TD(0) algorithm

Summary

Advanced Policy Estimation Algorithms

Advanced Policy Estimation Algorithms

TD(λ) algorithm

SARSA algorithm

Q-learning

Summary

Other Books You May Enjoy

Other Books You May Enjoy

Leave a review - let other readers know what you think

Customer Reviews

3.4 (5)

5 star

40%

4 star

20%

3 star

0

2 star

20%

1 star

20%

SARSA algorithm

SARSA (whose name is derived from the sequence state-action-reward-state-action) is a natural extension of TD(0) to the estimation of the Q function. Its standard formulation (which is sometimes called one-step SARSA, or SARSA(0), for the same reasons explained in the previous chapter) is based on a single next reward, r_t+1, which is obtained by executing the action a_t in the state s_t. The temporal difference computation is based on the following update rule:

The equation is equivalent to TD(0), and if the policy is chosen to be GLIE, it has been proven (in Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms, Singh S., Jaakkola T., Littman M. L., Szepesvári C., Machine Learning, 39/2000) that SARSA converges to an optimal policy, π^opt(s), with the probability 1, when all couples (state,...

Search

Your notes and bookmarks