An alternative approach to policy iteration is provided by the value iteration algorithm. The main assumption is based on the empirical observation that the policy evaluation step converges rather quickly and it's reasonable to stop the process after a fixed number of steps (normally 1). In fact, policy iteration can be imagined like a game where the first player tries to find the correct values considering a stable policy, while the other one creates a new policy that is greedy with respect to the new values. Clearly, the second step compromises the validity of the previous evaluation, forcing the first player to repeat the process. However, as the Bellman equation uses a single fixed point, the algorithm converges to a solution characterized by the fact that the policy doesn't change anymore and, consequently, the evaluation becomes stable. This process...
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Table Of Contents
Mastering Machine Learning Algorithms
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Mastering Machine Learning Algorithms
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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.
Table of Contents (17 chapters)
Preface
Free Chapter
Machine Learning Model Fundamentals
Introduction to Semi-Supervised Learning
Graph-Based Semi-Supervised Learning
Bayesian Networks and Hidden Markov Models
EM Algorithm and Applications
Hebbian Learning and Self-Organizing Maps
Clustering Algorithms
Ensemble Learning
Neural Networks for Machine Learning
Advanced Neural Models
Autoencoders
Generative Adversarial Networks
Deep Belief Networks
Introduction to Reinforcement Learning
Advanced Policy Estimation Algorithms
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