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.
Table of Contents (28 chapters)
26
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Index

Introduction to Reinforcement Learning

In this chapter, we're going to introduce the fundamental concepts of Reinforcement Learning (RL), which is a set of approaches that allows an agent to learn how to behave in an unknown environment thanks to rewards that are provided after each possible action. RL has been studied for decades, but it has matured into a powerful approach in the last few years, with advances making it possible to employ deep learning models together with standard (and often simple) algorithms in order to solve extremely complex problems (such as learning how to play an Atari game perfectly).

In particular, we will discuss:

  • The concept of the Markov Decision Process (MDP)
  • The concepts of environment, agent, policy, and reward
  • The policy iteration algorithm
  • The value iteration algorithm
  • The TD(0) algorithm

We can now introduce the main concepts that characterize a reinforcement learning scenario, focusing on the features of each...