Book Image

Mastering Java Machine Learning

By : Uday Kamath, Krishna Choppella
Book Image

Mastering Java Machine Learning

By: Uday Kamath, Krishna Choppella

Overview of this book

Java is one of the main languages used by practicing data scientists; much of the Hadoop ecosystem is Java-based, and it is certainly the language that most production systems in Data Science are written in. If you know Java, Mastering Machine Learning with Java is your next step on the path to becoming an advanced practitioner in Data Science. This book aims to introduce you to an array of advanced techniques in machine learning, including classification, clustering, anomaly detection, stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, deep learning, and big data batch and stream machine learning. Accompanying each chapter are illustrative examples and real-world case studies that show how to apply the newly learned techniques using sound methodologies and the best Java-based tools available today. On completing this book, you will have an understanding of the tools and techniques for building powerful machine learning models to solve data science problems in just about any domain.
Table of Contents (20 chapters)
Mastering Java Machine Learning
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Linear Algebra
Index

Appendix A. Linear Algebra

Linear algebra is of primary importance in machine learning and it gives us an array of tools that are especially handy for the purpose of manipulating data and extracting patterns from it. Moreover, when data must be processed in batches as in much machine learning, great runtime efficiencies are gained from using the "vectorized" form as an alternative to traditional looping constructs when implementing software solutions in optimization or data pre-processing or any number of operations in analytics.

We will consider only the domain of real numbers in what follows. Thus, a vector represents an array of n real-valued numbers. A matrix is a two-dimensional array of m rows and n columns of real-valued numbers.

Some key concepts from the foundation of linear algebra are presented here.