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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
Author Profile Icon Alexey Grigorev
Alexey Grigorev
Richard M. Reese Richard M. Reese
Author Profile Icon Richard M. Reese
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

Deep learning and regression analysis


Neural networks can be used to perform regression analysis. However, other techniques (see the early chapters) may offer a more effective solution. With regression analysis, we want to predict a result based on several input variables.

We can perform regression analysis using an output layer that consists of a single neuron that sums the weighted input plus bias of the previous hidden layer. Thus, the result is a single value representing the regression.

Preparing the data

We will use a car evaluation database to demonstrate how to predict the acceptability of a car based on a series of attributes. The file containing the data we will be using can be downloaded from: http://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data. It consists of car data such as price, number of passengers, and safety information, and an assessment of its overall quality. It is this latter element, quality, that we will try to predict. The comma-delimited values in...

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