Book Image

Mastering Java for Data Science

By : Alexey Grigorev
Book Image

Mastering Java for Data Science

By: Alexey Grigorev

Overview of this book

Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises. Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort. This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data. Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings.
Table of Contents (17 chapters)
Title Page
About the Author
About the Reviewers
Customer Feedback

Deep learning for cats versus dogs

While MNIST is a very good dataset for educational purpose, it is quite small. Let's take a look at a different image recognition problem: given a picture, we want to predict if there is a cat on the image or a dog.

For this, we will use the dataset with dogs and cats pictures from a competition run on kaggle, and the dataset can be downloaded from

Let's start by first reading the data.

Reading the data

For the dogs versus cats competition, there are two datasets; training, with 25,000 images of dogs and cats, 50% each, and testing. For the purposes of this chapter, we only need to download the training dataset. Once you have downloaded it, unpack it somewhere.

The filenames look like the following:

dog.9993.jpg dog.9994.jpg dog.9995.jpg

cat.10000.jpg cat.10001.jpg cat.10002.jpg

The label (dog or cat) is encoded into the filename.

As you know, the first thing we always do is to split the data into training and validation sets...