Hands-on artificial intelligence with Java for beginners : build intelligent apps using machine learning and deep learning with deeplearning4j / Nisheeth Joshi.

Format:

Book

Author/Creator:

Joshi, Nisheeth, author.

Language:

English

Subjects (All):

JavaScript (Computer program language).

Physical Description:

1 online resource (140 pages) : illustrations

Edition:

1st edition

Place of Publication:

Birmingham ; Mumbai : Packt, 2018.

System Details:

text file

Biography/History:

Joshi Nisheeth: Nisheeth Joshi is an associate professor and a researcher at Banasthali University. He has also done a PhD in Natural Language Processing. He is an expert with the TDIL Program, Department of IT, Government of India, the premier organization overseeing language technology funding and research in India. He has several publications to his name in various journals and conferences, and also serves on the program committees and editorial boards of several conferences and journals.

Summary:

Build, train, and deploy intelligent applications using Java libraries Key Features Leverage the power of Java libraries to build smart applications Build and train deep learning models for implementing artificial intelligence Learn various algorithms to automate complex tasks Book Description Artificial intelligence (AI) is increasingly in demand as well as relevant in the modern world, where everything is driven by technology and data. AI can be used for automating systems or processes to carry out complex tasks and functions in order to achieve optimal performance and productivity. Hands-On Artificial Intelligence with Java for Beginners begins by introducing you to AI concepts and algorithms. You will learn about various Java-based libraries and frameworks that can be used in implementing AI to build smart applications. In addition to this, the book teaches you how to implement easy to complex AI tasks, such as genetic programming, heuristic searches, reinforcement learning, neural networks, and segmentation, all with a practical approach. By the end of this book, you will not only have a solid grasp of AI concepts, but you'll also be able to build your own smart applications for multiple domains. What you will learn Leverage different Java packages and tools such as Weka, RapidMiner, and Deeplearning4j, among others Build machine learning models using supervised and unsupervised machine learning techniques Implement different deep learning algorithms in Deeplearning4j and build applications based on them Study the basics of heuristic searching and genetic programming Differentiate between syntactic and semantic similarity among texts Perform sentiment analysis for effective decision making with LingPipe Who this book is for Hands-On Artificial Intelligence with Java for Beginners is for Java developers who want to learn the fundamentals of artificial intelligence and extend their programming knowledge to build smarter applications.

Contents:

Cover

Title Page

Copyright and Credits

Packt Upsell

Contributors

Table of Contents

Preface

Chapter 1: Introduction to Artificial Intelligence and Java

What is machine learning?

Differences between classification and regression

Installing JDK and JRE

Setting up the NetBeans IDE

Importing Java libraries and exporting code in projects as a JAR file

Summary

Chapter 2: Exploring Search Algorithms

An introduction to searching

Implementing Dijkstra's search

Understanding the notion of heuristics

A brief introduction to the A* algorithm

Implementing an A* algorithm

Chapter 3: AI Games and the Rule-Based System

Introducing the min-max algorithm

Implementing an example min-max algorithm

Installing Prolog

An introduction to rule-based systems with Prolog

Setting up Prolog with Java

Executing Prolog queries using Java

Chapter 4: Interfacing with Weka

An introduction to Weka

Installing and interfacing with Weka

Calling the Weka environment into Java

Reading and writing datasets

Converting datasets

Converting an ARFF file to a CSV file

Converting a CSV file to an ARFF file

Chapter 5: Handling Attributes

Filtering attributes

Discretizing attributes

Attribute selection

Chapter 6: Supervised Learning

Developing a classifier

Model evaluation

Making predictions

Loading and saving models

Chapter 7: Semi-Supervised and Unsupervised Learning

Working with k-means clustering

Evaluating a clustering model

An introduction to semi-supervised learning

The difference between unsupervised and semi-supervised learning

Self-training and co-training machine learning models

Downloading a semi-supervised package

Creating a classifier for semi-supervised models.

Making predictions with semi-supervised machine learning models

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Index.

Notes:

Includes index.

Description based on print version record.

ISBN:

9781789531022

1789531020

OCLC:

1055555822