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(λ) algorithm


In the previous chapter, we introduced the temporal difference strategy, and we discussed a simple example called TD(0). In the case of TD(0), the discounted reward is approximated by using a one-step backup. Hence, if the agent performs an action at in the state st, and the transition to the state st+1 is observed, the approximation becomes the following:

If the task is episodic (as in many real-life scenarios) and has T(ei) steps, the complete backup for the episode ei is as follows:

The previous expression ends when the MDP process reaches an absorbing state; therefore, Rt is the actual value of the discounted reward. The difference between TD(0) and this choice is clear: in the first case, we can update the value function after each transition, whereas with a complete backup, we need to wait for the end of the episode. We can say that this method (which is called Monte Carlo, because it's based on the idea of averaging the overall reward of an entire sequence) is exactly...