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

TD(0) algorithm


One of the problems with Dynamic Programming algorithms is the need for a 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, the states can be discovered by letting the agent explore the environment, but the 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 agent...