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

Hand written digit recognizition using CNN

This is one of the classic "Hello World" type problem in the field of deep learning. We already covered one very simple case study of flower classification earlier and in this one we are going to classify hand written digits. For this case study we are using the MNIST dataset. The MNIST database of handwritten digits is available at It has a training set of 60,000 examples, and a test set of 10,000 examples. Some of the sample images in this dataset are as shown:

A typical hello world neural network that we are building is to train our network with the training set and to classify the images based on the test set. For this we will use a CNN or convolutional neural network.

A convolutional neural network is a special type of feed forward neural network and is especially suited for image classification. Explaining the entire concept of a convolution network is beyond scope of this chapter but we will explain it briefly...