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

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(λ), is extremely powerful, and it assures a 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 further 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 a visual input and outputs...