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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

Using neural networks in data science


An Artificial Neural Network (ANN), which we will call a neural network, is based on the neuron found in the brain. A neuron is a cell that has dendrites connecting it to input sources and other neurons. Depending on the input source, a weight allocated to a source, the neuron is activated, and then fires a signal down a dendrite to another neuron. A collection of neurons can be trained to respond to a set of input signals.

 

An artificial neuron is a node that has one or more inputs and a single output. Each input has a weight assigned to it that can change over time. A neural network can learn by feeding an input into a network, invoking an activation function, and comparing the results. This function combines the inputs and creates an output. If outputs of multiple neurons match the expected result, then the network has been trained correctly. If they don't match, then the network is modified.

A neural network can be visualized as shown in the following...

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