Build next-generation artificial intelligence systems with Java

Key Features

• Implement AI techniques to build smart applications using Deeplearning4j
• Perform big data analytics to derive quality insights using Spark MLlib
• Create self-learning systems using neural networks, NLP, and reinforcement learning

Book Description

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 (NLP), image recognition, genetic algorithms, and fuzzy logic systems.

What you will learn

• Manage artificial intelligence techniques for big data with Java
• Build smart systems to analyze data for enhanced customer experience
• Learn to use artificial intelligence frameworks for big data
• Understand complex problems with algorithms and neuro-fuzzy systems
• Design stratagems to leverage data using machine learning process
• Apply deep learning techniques to prepare data for modeling
• Construct models that learn from data using open source tools
• Analyze big data problems using scalable machine learning algorithms

Who This Book Is For

Artificial Intelligence for Big Data is for data scientists, big data professionals, or novices who have basic knowledge of big data and wish to get proficiency in artificial intelligence techniques for big data. Some competence in mathematics is an added advantage in the field of elementary linear algebra and calculus.

Table of Contents

1. Big Data and Artificial Intelligence systems
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