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

Deep learning

In the last section, we saw how a number of perceptrons can be stacked together in multiple layers to start a learning network. We saw an example of a feed forward network with just one hidden layer. Apart from just a single hidden layer, we can have multiple hidden layers stacked one after the other. This would enhance the accuracy of the artificial neural network further. When an artificial neural network has multiple hidden layers (that is, greater than one), this approach is called deep learning as the network is deep.

Deep learning is currently one of the most widely studied research topics and it is practically used in many real-world applications.

Let's now see some of the advantages and real-world use cases of deep learning.

Advantages and use cases of deep learning

There are two main advantages of deep learning:

  1. No feature engineering required: In traditional machine learning, feature engineering is of the utmost importance if you want your models to work well. There are...