Book Image

Mastering Machine Learning Algorithms

Book Image

Mastering Machine Learning Algorithms

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 (22 chapters)
Title Page
Dedication
Packt Upsell
Contributors
Preface
13
Deep Belief Networks
Index

Policy iteration


In this section, we are going to analyze a strategy to find an optimal policy based on a complete knowledge of the environment (in terms of transition probability and expected returns). The first step is to define a method that can be employed to build a greedy policy. Let's suppose we're working with a finite MDP and a generic policy, π; we can define the intrinsic value of a state, st, as the expected discounted return obtained by the agent starting from st and following the stochastic policy, π:

In this case, we are assuming that, as the agent will follow π, state sa is more useful than sb if the expected return starting from sa is greater than the one obtained starting from sb. Unfortunately, trying to directly find the value of each state using the previous definition is almost impossible when γ > 0. However, this a problem that can be solved using Dynamic Programming (for further information, please refer to Dynamic Programming and Markov Process, Ronald A. Howard...