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Artificial Intelligence for Big Data

You're reading from Artificial Intelligence for Big Data Complete guide to automating Big Data solutions using Artificial Intelligence techniques

Product type Paperback

Published in May 2018

Publisher Packt

ISBN-13 9781788472173

Length 384 pages

Edition 1st Edition

Languages

Java

Tools

TensorFlow

Concepts

Artificial Intelligence

Authors (2):

Anand Deshpande

Manish Kumar

View More author details

Table of Contents (14) Chapters

Preface

1. Big Data and Artificial Intelligence Systems FREE CHAPTER

2. Ontology for Big Data

3. Learning from Big Data

4. Neural Network for Big Data

5. Deep Big Data Analytics

6. Natural Language Processing

7. Fuzzy Systems

8. Genetic Programming

9. Swarm Intelligence

10. Reinforcement Learning

11. Cyber Security

12. Cognitive Computing

13. Other Books You May Enjoy

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Opt4J library

Opt4J is a modular framework for meta-heuristic optimization that can be applied to a range of evolutionary algorithms. In the context of this chapter, we are looking at implementing SI algorithms such as ACO and PSO using the library. The libraries that deal with optimization problems have three primary components at abstract level. Creator, decoder, and evaluator. The creator provides random genotypes (please refer to Chapter 8, Genetic Programming, for details on genotype and phenotypes) from the search space. They represent agents in case of SI algorithms. The agents are created by the creator object.

The Opt4J library provides an org.opt4j.optimizers.mopso.Particle class that works as a creator. The agents within the swarm are the instances of this class that are actually created by a factory class' org.opt4j.optimizers.mopso.ParticleFactory. The decoder transforms a genotype to a phenotype. The decoder converts the abstract characteristics into tangible objects and associate...

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Authors (2)

Deshpande

Deshpande

Tanmay Deshpande is a Hadoop and big data evangelist. He currently works with Schlumberger as a Big Data Architect in Pune, India. He has interest in a wide range of technologies, such as Hadoop, Hive, Pig, NoSQL databases, Mahout, Sqoop, Java, cloud computing, and so on. He has vast experience in application development in various domains, such as oil and gas, finance, telecom, manufacturing, security, and retail. He enjoys solving machine-learning problems and spends his time reading anything that he can get his hands on. He has great interest in open source technologies and has been promoting them through his talks. Before Schlumberger, he worked with Symantec, Lumiata, and Infosys. Through his innovative thinking and dynamic leadership, he has successfully completed various projects. He regularly blogs on his website http://hadooptutorials.co.in. You can connect with him on LinkedIn at https://www.linkedin.com/in/deshpandetanmay/. He has also authored Mastering DynamoDB, published in August 2014, DynamoDB Cookbook, published in September 2015, Hadoop Real World Solutions Cookbook-Second Edition, published in March 2016, Hadoop: Data Processing and Modelling, published in August, 2016, and Hadoop Blueprints, published in September 2016, all by Packt Publishing.

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Kumar

Kumar

Ashish Kumar is a seasoned data science professional, a publisher author and a thought leader in the field of data science and machine learning. An IIT Madras graduate and a Young India Fellow, he has around 7 years of experience in implementing and deploying data science and machine learning solutions for challenging industry problems in both hands-on and leadership roles. Natural Language Procession, IoT Analytics, R Shiny product development, Ensemble ML methods etc. are his core areas of expertise. He is fluent in Python and R and teaches a popular ML course at Simplilearn. When not crunching data, Ashish sneaks off to the next hip beach around and enjoys the company of his Kindle. He also trains and mentors data science aspirants and fledgling start-ups.

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