O'Reilly logo

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Hands-On Artificial Intelligence with Java for Beginners

Book Description

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.

Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.

Table of Contents

  1. Title Page
  2. Copyright and Credits
    1. Hands-On Artificial Intelligence with Java for Beginners
  3. Packt Upsell
    1. Why subscribe?
    2. PacktPub.com
  4. Contributors
    1. About the author
    2. Packt is searching for authors like you
  5. Preface
    1. Who this book is for
    2. What this book covers
    3. To get the most out of this book
      1. Download the example code files
      2. Conventions used
    4. Get in touch
      1. Reviews
  6. Introduction to Artificial Intelligence and Java
    1. What is machine learning?
    2. Differences between classification and regression
    3. Installing JDK and JRE
    4. Setting up the NetBeans IDE
    5. Importing Java libraries and exporting code in projects as a JAR file
    6. Summary
  7. Exploring Search Algorithms
    1. An introduction to searching
    2. Implementing Dijkstra's search
    3. Understanding the notion of heuristics
    4. A brief introduction to the A* algorithm
    5. Implementing an A* algorithm
    6. Summary
  8. AI Games and the Rule-Based System
    1. Introducing the min-max algorithm
    2. Implementing an example min-max algorithm
    3. Installing Prolog
    4. An introduction to rule-based systems with Prolog
    5. Setting up Prolog with Java
    6. Executing Prolog queries using Java
    7. Summary
  9. Interfacing with Weka
    1. An introduction to Weka
    2. Installing and interfacing with Weka
      1. Calling the Weka environment into Java
    3. Reading and writing datasets
    4. Converting datasets
      1. Converting an ARFF file to a CSV file
      2. Converting a CSV file to an ARFF file
    5. Summary
  10. Handling Attributes
    1. Filtering attributes
    2. Discretizing attributes
    3. Attribute selection
    4. Summary
  11. Supervised Learning
    1. Developing a classifier
    2. Model evaluation
    3. Making predictions
    4. Loading and saving models
    5. Summary
  12. Semi-Supervised and Unsupervised Learning
    1. Working with k-means clustering
    2. Evaluating a clustering model
    3. An introduction to semi-supervised learning
    4. The difference between unsupervised and semi-supervised learning
    5. Self-training and co-training machine learning models
      1. Downloading a semi-supervised package
      2. Creating a classifier for semi-supervised models
    6. Making predictions with semi-supervised machine learning models
    7. Summary
  13. Other Books You May Enjoy
    1. Leave a review - let other readers know what you think