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

Restricted Boltzmann Machines


RBM is often used as part of a multi-layer deep belief network. The output of the RBM is used as an input to another layer. The use of the RBM is repeated until the final layer is reached.

Note

Deep Belief Networks (DBNs) consist of several RBMs stacked together. Each hidden layer provides the input for the subsequent layer. Within each layer, the nodes cannot communicate laterally and it becomes essentially a network of other single-layer networks. DBNs are especially helpful for classifying, clustering, and recognizing image data.

The term, continuous restricted Boltzmann machine, refers an RBM that uses values other than integers. Input data is normalized to values between zero and one.

Each node of the input layer is connected to each node of the second layer. No nodes of the same layer are connected to each other. That is, there is no intra-layer communication. This is what restricted means.

The number of input nodes for the visible layer is dependent on the...