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

Multi-layer feed-forward neural network


Historically, artificial neural networks have been largely identified by multi-layer feed-forward perceptrons, and so we will begin with a discussion of the primitive elements of the structure of such networks, how to train them, the problem of overfitting, and techniques to address it.

Inputs, neurons, activation function, and mathematical notation

A single neuron or perceptron is the same as the unit described in the Linear Regression topic in Chapter 2, Practical Approach to Real-World Supervised Learning. In this chapter, the data instance vector will be represented by x and has d dimensions, and each dimension can be represented as . The weights associated with each dimension are represented as a weight vector w that has d dimensions, and each dimension can be represented as . Each neuron has an extra input b, known as the bias, associated with it.

Neuron pre-activation performs the linear transformation of inputs given by:

The activation function...