Artificial Intelligence for Big Data

Key Features Implement Artificial Intelligence techniques to build smart applications using Deeplearning4j a Java deep learning library Create smart self learning and decision making systems Develop smarter Big data applications to get better insights from your analysis Book Description
In the age of big data, companies have growing amount of consumer data than ever before - far more than human and their current technologies can ever hope to keep up with. Artificial Intelligence closes the gap by moving far past human limitations to consume and analyze data.
This book will help you create smart systems to extract intelligent insights for decision making. We will learn about widely used Artificial Intelligence techniques for carrying out solutions in a production ready environment. Explore advanced topics like neural network, clustering, symbolic and sub-symbolic information representation and many more.
Later we will use machine learning algorithms such as K-means, SVM, RBF, regression to perform advance data analysis. We will take you through an understanding of the current status of Machine and Deep Learning techniques to Genetic and Neuro-Fuzzy algorithms. This book aims to address how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help to solve real world problems.
By the end of this book, you'll learn how to implement various Artificial Intelligence algorithms for your big data systems and integrate into your product offerings such as search, image recognition and language processing, Genetic Algorithms, and Fuzzy Logic systems What you will learn Manage Artificial Intelligence techniques for Big Data with Java. Build smart systems that can analyze vast and growing data to apply the results for enhanced customer experiences Learn to use Artificial Intelligence Frameworks for Big Data. Understand and manage complex problems with Genetic Algorithms and Neuro-Fuzzy Systems Design an approach to leverage data using the steps in the machine learning process. Apply deep learning techniques to explore and prepare data for modeling. Construct models that learn from data using widely available open source tools Analyze big data problems using scalable machine learning algorithms on Spark.