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Java for Data Science

Java for Data Science

By : Richard M. Reese , Reese
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Java for Data Science

Java for Data Science

By: Richard M. Reese , 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)
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Java for Data Science
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Deeplearning4j architecture


In this section, we will discuss its architecture and address several of the common tasks performed when using the API. DLN typically starts with the creation of a MultiLayerConfiguration instance, which defines the network, or model. The network is composed of multiple layers. Hyperparameters are used to configure the network and are variables that affect such things as learning speed, activation functions to use for a layer, and how weights are to be initialized.

As with neural networks, the basic DLN process consists of:

  • Acquiring and manipulating data

  • Configuring and building a model

  • Training the model

  • Testing the model

We will investigate each of these tasks in the next sections.

Note

The code examples in this section are not intended to be entered and executed here. Instead, these examples are snippets out of later models that we will be using.

Acquiring and manipulating data

The DL4J API has a number of techniques for acquiring data. We will focus on those specific...

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