Book Image

Artificial Intelligence for Big Data

By : Anand Deshpande, Manish Kumar
Book Image

Artificial Intelligence for Big Data

By: Anand Deshpande, Manish Kumar

Overview of this book

In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Reinforcement learning techniques


With this background in reinforcement learning, in the next few sections we are going to look at some of the formal techniques for exploration into the search space with the goal of maximizing the rewards in an optimal way. 

Markov decision processes

In order to understand the Markov decision processes (MDPs), let us define two environment types:

  • A deterministic environment: In a deterministic environment, an action taken within a particular state of the environment determines a certain outcome. For example, in the game of chess out of all the possible moves at the beginning of the game, when we move a pawn from e4 to e5, the immediate next step is certain and does not differ across various games. There is also a level of certainty of reward in a deterministic environment along with the next possible state(s).
  • A stochastic environment: In the case of a stochastic environment, there is always a level of randomness and uncertainty in terms of next state of the...