Book Image

Big Data Analytics with Java

Book Image

Big Data Analytics with Java


Overview of this book

This book covers case studies such as sentiment analysis on a tweet dataset, recommendations on a movielens dataset, customer segmentation on an ecommerce dataset, and graph analysis on actual flights dataset. This book is an end-to-end guide to implement analytics on big data with Java. Java is the de facto language for major big data environments, including Hadoop. This book will teach you how to perform analytics on big data with production-friendly Java. This book basically divided into two sections. The first part is an introduction that will help the readers get acquainted with big data environments, whereas the second part will contain a hardcore discussion on all the concepts in analytics on big data. It will take you from data analysis and data visualization to the core concepts and advantages of machine learning, real-life usage of regression and classification using Naïve Bayes, a deep discussion on the concepts of clustering,and a review of simple neural networks on big data using deepLearning4j or plain Java Spark code. This book is a must-have book for Java developers who want to start learning big data analytics and want to use it in the real world.
Table of Contents (21 chapters)
Big Data Analytics with Java
About the Author
About the Reviewers
Customer Feedback
Free Chapter
Big Data Analytics with Java
Ensembling on Big Data
Real-Time Analytics on Big Data


This chapter gave a brief introduction to the field of deep learning for developers. We started with how an artificial neural network mimics the working of our own nervous system. We showed the basic unit of this artificial neural network, the perceptron. We showed how perceptrons can be used to depict logical functions and we later moved on to show their pitfalls. Later, we learnt how the perceptron's usage can be enhanced by making modifications to it, leading us to the artificial neuron, the sigmoid neuron. Further we also covered a sample case study for the classification of Iris flower species based on the features that were used to train our neural network. We also mentioned how the Java library Deeplearning4j includes many deep learning algorithms that can be integrated with Apache Spark on the Java big data stack. Finally, we provided readers with information on where they can find free resources to learn more.