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Mastering Clojure Data Analysis

You're reading from   Mastering Clojure Data Analysis If you'd like to apply your Clojure skills to performing data analysis, this is the book for you. The example based approach aids fast learning and covers basic to advanced topics. Get deeper into your data.

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Product type Paperback
Published in May 2014
Publisher
ISBN-13 9781783284139
Length 340 pages
Edition Edition
Languages
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Author (1):
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Eric Richard Rochester Eric Richard Rochester
Author Profile Icon Eric Richard Rochester
Eric Richard Rochester
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Toc

Table of Contents (17) Chapters Close

Mastering Clojure Data Analysis
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Network Analysis – The Six Degrees of Kevin Bacon FREE CHAPTER 2. GIS Analysis – Mapping Climate Change 3. Topic Modeling – Changing Concerns in the State of the Union Addresses 4. Classifying UFO Sightings 5. Benford's Law – Detecting Natural Progressions of Numbers 6. Sentiment Analysis – Categorizing Hotel Reviews 7. Null Hypothesis Tests – Analyzing Crime Data 8. A/B Testing – Statistical Experiments for the Web 9. Analyzing Social Data Participation 10. Modeling Stock Data Index

Analyzing both text and stock features together with neural nets


We now have everything ready to perform the analysis, except for the engine that will actually attempt to learn the training data.

In this instance, we're going to try to train an artificial neural network to learn the direction of change of the future prices of the input data. In other words, we'll try to train it to tell whether the price will go up or down in the near future. We want to create a simple binary classifier from the past price changes and the text of an article.

Understanding neural nets

As the name implies, artificial neural networks are machine learning structures modeled on the architecture and behavior of neurons, such as the ones found in the human brain. Artificial neural networks come in many forms, but today we're going to use one of the oldest and most common forms: the three-layer feed-forward network.

We can see the structure of a unit outlined in the following figure:

Each unit is able to realize linearly...

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