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

Frequently asked questions


Q: What is the difference between distributed computing paradigm and swarm intelligence? In the case of distributed computing, we also divide the work units in chunks that are processed by individual nodes. 

A: The basic difference between these two types of systems is that the distributed computing systems are centrally controlled. There is a master node or processing unit that keeps track of all the worker nodes and allocated work units based on their availability. The frameworks also maintain a level of redundancy so that the system is reliable in case of failure of one of the worker nodes. In case of intelligent swarm behavior demonstrated by social creatures, there is not centralized control and all the agents operate independently within their operating principles. The agents are self-organizing and collaborate intuitively and implicitly instead of an explicit collaboration managed by a central controlling unit. 

Q: How do systems based on SI algorithms mimic...