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Java for Data Science

You're reading from   Java for Data Science Examine the techniques and Java tools supporting the growing field of data science

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Product type Paperback
Published in Jan 2017
Publisher Packt
ISBN-13 9781785280115
Length 386 pages
Edition 1st Edition
Languages
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Authors (2):
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Jennifer L. Reese Jennifer L. Reese
Author Profile Icon Jennifer L. Reese
Jennifer L. Reese
Richard M. Reese Richard M. Reese
Author Profile Icon Richard M. Reese
Richard M. Reese
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Data Science 2. Data Acquisition FREE CHAPTER 3. Data Cleaning 4. Data Visualization 5. Statistical Data Analysis Techniques 6. Machine Learning 7. Neural Networks 8. Deep Learning 9. Text Analysis 10. Visual and Audio Analysis 11. Mathematical and Parallel Techniques for Data Analysis 12. Bringing It All Together

Chapter 7. 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...

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