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

Chapter 7. Deep Learning

In Chapter 2, Practical Approach to Real-World Supervised Learning, we discussed different supervised classification techniques that are general and can be used in a wide range of applications. In the area of supervised non-linear techniques, especially in computer-vision, deep learning and its variants are having a remarkable impact. We find that deep learning and associated methodologies can be applied to image-recognition, image and object annotation, movie descriptions, and even areas such as text classification, language modeling, translations, and so on. (References [1, 2, 3, 4, and 5])

To set the stage for deep learning, we will start with describing what neurons are and how they can be arranged to build multi-layer neural networks, present the core elements of these networks, and explain how they work. We will then discuss the issues and problems associated with neural networks that gave rise to advances and structural changes in deep learning. We will learn...