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: Why do we need fuzzy systems?

A: In our quest to build intelligent machines, we cannot continue to model the world with crisp or quantitative and definite inputs. We need to model systems like the human brain, which can easily understand and process input, even if they are not mathematical and contain a degree of vagueness. We need fuzzy systems in order to interpret real-world input and produce prescribed actions based on the context. Fuzzy systems can fuzzify and defuzzify the input and facilitate inseparability between natural events and computers. 

Q: What are crisp sets and fuzzy sets? How are they different from one another?

A: Crisp sets have two possibilities for members. A particular element/data point/event is a member or a non-member of the crisp set. For example, days in a week from Monday to Sunday are members of the days of the week crisp set. Anything else apart from the seven days is not a member of the set. Members of fuzzy sets, on the other hand...