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Java: Data Science Made Easy

You're reading from   Java: Data Science Made Easy Data collection, processing, analysis, and more

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Product type Course
Published in Jul 2017
Publisher Packt
ISBN-13 9781788475655
Length 734 pages
Edition 1st Edition
Languages
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Authors (3):
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Alexey Grigorev Alexey Grigorev
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Alexey Grigorev
Richard M. Reese Richard M. Reese
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Richard M. Reese
Jennifer L. Reese Jennifer L. Reese
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Jennifer L. Reese
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Toc

Table of Contents (29) Chapters Close

Title Page
Credits
Preface
1. Module 1 FREE CHAPTER
2. Getting Started with Data Science 3. Data Acquisition 4. Data Cleaning 5. Data Visualization 6. Statistical Data Analysis Techniques 7. Machine Learning 8. Neural Networks 9. Deep Learning 10. Text Analysis 11. Visual and Audio Analysis 12. Visual and Audio Analysis 13. Mathematical and Parallel Techniques for Data Analysis 14. Bringing It All Together 15. Module 2
16. Data Science Using Java 17. Data Processing Toolbox 18. Exploratory Data Analysis 19. Supervised Learning - Classification and Regression 20. Unsupervised Learning - Clustering and Dimensionality Reduction 21. Working with Text - Natural Language Processing and Information Retrieval 22. Extreme Gradient Boosting 23. Deep Learning with DeepLearning4J 24. Scaling Data Science 25. Deploying Data Science Models 26. Bibliography

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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