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)
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In this chapter, we introduced the most important RL concepts, focusing on the mathematical structure of an environment as an MDP, and on the different kinds of policy and how they can be derived from the expected reward obtained by an agent. In particular, we defined the value of a state as the expected future reward considering a sequence discounted by a factor, γ. In the same way, we introduced the concept of the Q function, which is the value of an action when the agent is in a specific state.

These concepts directly employed the policy iteration algorithm, which is based on a Dynamic Programming approach assuming complete knowledge of the environment. The task is split into two stages; during the first one, the agent evaluates all the states given the current policy, while in the second one, the policy is updated in order to be greedy with respect to the new value function.

In this way, the agent is forced to always pick the action that leads...