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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

2. Ontology for Big Data FREE CHAPTER

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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Ant colony optimization model

The ant colony optimization (ACO) is another widely used and adapted variation of the SI algorithms. At its minimum, the objective of the ant colony or the artificial agent swarms is to set out in search for an asset (food in case of ants and a package in case of a robot colony in a retailer warehouse) in an optimum way so as to traverse minimum distance to and from the asset and the base location. This model is useful with surveillance drones, autonomous car route planning, and so on.

Let us understand some of the operating principles in an ant colony and get introduced to the terminologies so that those can be applied in designing artificial swarms based on the ACO model. Here is a figure of an Ant colony and a Food source:

Figure 9.4 Ant colony representation and terms

In this example, there is food source in the vicinity of the ant colony. There are two paths to the food. Path-1 is a long distance to the Food source and Path-2 is the shortest distance. The...

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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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