Book Image

Java Data Science Cookbook

By : Rushdi Shams
Book Image

Java Data Science Cookbook

By: Rushdi Shams

Overview of this book

If you are looking to build data science models that are good for production, Java has come to the rescue. With the aid of strong libraries such as MLlib, Weka, DL4j, and more, you can efficiently perform all the data science tasks you need to. This unique book provides modern recipes to solve your common and not-so-common data science-related problems. We start with recipes to help you obtain, clean, index, and search data. Then you will learn a variety of techniques to analyze, learn from, and retrieve information from data. You will also understand how to handle big data, learn deeply from data, and visualize data. Finally, you will work through unique recipes that solve your problems while taking data science to production, writing distributed data science applications, and much more - things that will come in handy at work.
Table of Contents (16 chapters)
Java Data Science Cookbook
About the Author
About the Reviewer
Customer Feedback

Creating a deep autoencoder using Deep Learning for Java (DL4j)

A deep autoencoder is a deep neural network that is composed of two deep-belief networks that are symmetrical. The networks usually have two separate four or five shallow layers (restricted Boltzmann machines) representing the encoding and decoding half of the net. In this recipe, you will be developing a deep autoencoder consisting of one input layer, four decoding layers, four encoding layers, and one output layer. In doing so, we will be using a very popular dataset named MNIST.


To learn more about MNIST, visit If you want to know more about deep autoencoders, visit to complete the command. Close windows opened along the way. command. command. and click Other... until you reach the following window. In this window, fill out the Group Id and Artifact Id as follows or with anything you like. Click on Finish.

How to do it...

  1. Start by creating a...