Book Image

Java for Data Science

By : Richard M. Reese, Jennifer L. Reese
Book Image

Java for Data Science

By: Richard M. Reese, Jennifer L. Reese

Overview of this book

para 1: Get the lowdown on Java and explore big data analytics with Java for Data Science. Packed with examples and data science principles, this book uncovers the techniques & Java tools supporting data science and machine learning. Para 2: The stability and power of Java combines with key data science concepts for effective exploration of data. By working with Java APIs and techniques, this data science book allows you to build applications and use analysis techniques centred on machine learning. Para 3: Java for Data Science gives you the understanding you need to examine the techniques and Java tools supporting big data analytics. These Java-based approaches allow you to tackle data mining and statistical analysis in detail. Deep learning and Java data mining are also featured, so you can explore and analyse data effectively, and build intelligent applications using machine learning. para 4: What?s Inside ? Understand data science principles with Java support ? Discover machine learning and deep learning essentials ? Explore data science problems with Java-based solutions
Table of Contents (19 chapters)
Java for Data Science
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Implementing basic matrix operations


There are several different types of matrix operations, including simple addition, subtraction, scalar multiplication, and various forms of multiplication. To illustrate the matrix operations, we will focus on what is known as matrix product. This is a common approach that involves the multiplication of two matrixes to produce a third matrix.

Consider two matrices, A and B, where matrix A has n rows and m columns. Matrix B will have m rows and p columns. The product of A and B, written as AB, is an n row and p column matrix. The m entries of the rows of A are multiplied by the m entries of the columns of matrix B. This is more explicitly shown here, where:

Where the product is defined as follows:

We start with the declaration and initialization of the matrices. The variables n, m, p represent the dimensions of the matrices. The A matrix is n by m, the B matrix is m by p, and the C matrix representing the product is n by p:

int n = 4; 
int m = 2; 
...