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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
Author Profile Icon Jennifer L. Reese
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

Chapter 8. Neural Networks

While neural networks have been around for a number of years, they have grown in popularity due to improved algorithms and more powerful machines. Some companies are building hardware systems that explicitly mimic neural networks (https://www.wired.com/2016/05/google-tpu-custom-chips/). The time has come to use this versatile technology to address data science problems.

In this chapter, we will explore the ideas and concepts behind neural networks and then demonstrate their use. Specifically, we will:

  • Define and illustrate neural networks
  • Describe how they are trained
  • Examine various neural network architectures
  • Discuss and demonstrate several different neural networks, including:
    • A simple Java example
    • A Multi Layer Perceptron (MLP) network
    • The k-Nearest Neighbor (k-NN) algorithm and others

The idea for an Artificial Neural Network (ANN), which we will call a neural network, originates from the neuron found in the brain. A neuron is a cell that has dendrites connecting...

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