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)
Other Books You May Enjoy

The TD(0) algorithm

One of the problems with dynamic programming algorithms is the need for full knowledge of the environment in terms of states and transition probabilities. Unfortunately, there are many cases where these pieces of information are unknown before the direct experience. In particular, states can be discovered by letting the agent explore the environment, but transition probabilities require us to count the number of transitions to a certain state and this is often impossible. Moreover, an environment with absorbing states can prevent visiting many states if the agent has learned a good initial policy. For example, in a game, which can be described as an episodic MDP, the agent discovers the environment while learning how to move forward without ending in a negative absorbing state.

A general solution to these problems is provided by a different evaluation strategy, called Temporal Difference (TD) RL. In this case, we start with an empty value matrix and we let the...