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 algorithms concept


Let's create a simplistic model for reinforcement learning with an introduction of the basic terminologies

At each step and time (t), the agent:

  • Executes action at
  • Receives observation ot
  • Receives a reward rt

At each step and time (t), the environment:

  • Receives action at
  • Generates observation ot+1
  • Generates scalar reward rt+1

The environment is considered to be non-deterministic (action at based on ot will receive reward rt and the same action in the same state may result in different rewards). 

The agent (intelligent machine) is connected to the environmental context with its observation and action. The agent perceives the environment in a unique-to-itself manner and decides the action based on some of the popular and evolving techniques. At each step in time, the agent receives signals that represent the state of the environment.

The agent responds with an action that is one among several possible options at that point in time. The action generates an output...