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

Summary


The history of Deep Learning is intimately tied to the limitations of earlier attempts at using neural networks in machine learning and AI, and how these limitations were overcome with newer techniques, technological improvements, and the availability of vast amounts of data.

The perceptron is the basic neural network. Multi-layer networks are used in supervised learning and are built by connecting several hidden layers of neurons to propagate activations forward and using backpropagation to reduce the training error. Several activation functions are used, most commonly, the sigmoid and tanh functions.

The problems of neural networks are vanishing or exploding gradients, slow training, and the trap of local minima.

Deep learning successfully addresses these problems with the help of several effective techniques that can be used for unsupervised as well as supervised learning.

Among the building blocks of deep learning networks are Restricted Boltzmann Machines (RBM), Autoencoders, and...