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

Summary


In this chapter, we have explored one of the most important machine learning techniques, RL. We understood the difference between RL and supervised learning. Learning based on behavioral reinforcement for the agent is extremely critical in modeling the intelligent machines that will bridge the gap between human capabilities and the intelligent machines. We have seen the basic concepts of the RL algorithm along with the participating components. We have also tried to establish mathematical equations for a generic RL algorithm where the overall goal is to maximize cumulative rewards for the agent as it transitions through various states with every action.

We have briefly tried to understand the MDPs in a deterministic and stochastic environment. We also explored dynamic programming concepts in brief along with Q-learning and SARSA learning algorithms. In the end, we briefly discussed deep reinforcement learning DRL as a combination of deep neural networks and the reinforcement learning...