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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27
Index

Summary

In this chapter, we presented the natural evolution of TD(0) based on an average of backups with different lengths. The algorithm, called TD(), is extremely powerful, and it ensures faster convergence than TD(0), with only a few (non-restrictive) conditions. We also showed how to implement the Actor-Critic method with TD(0) in order to learn about both a stochastic policy and a value function.

In later sections, we discussed two methods based on the estimation of the Q function: SARSA and Q-learning. They are very similar, but the latter has a greedy approach, and its performance (in particular the training speed) results in it being superior to SARSA. The Q-learning algorithm is one of the most important models for the latest developments. In fact, it was the first RL approach employed with a deep convolutional network to solve complex environments (like Atari games). For this reason, we also presented a simple example based on an MLP that processes visual input and outputs...