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Learning Data Mining with Python

You're reading from   Learning Data Mining with Python Use Python to manipulate data and build predictive models

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781787126787
Length 358 pages
Edition 2nd Edition
Languages
Concepts
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Toc

Table of Contents (14) Chapters Close

Preface 1. Getting Started with Data Mining FREE CHAPTER 2. Classifying with scikit-learn Estimators 3. Predicting Sports Winners with Decision Trees 4. Recommending Movies Using Affinity Analysis 5. Features and scikit-learn Transformers 6. Social Media Insight using Naive Bayes 7. Follow Recommendations Using Graph Mining 8. Beating CAPTCHAs with Neural Networks 9. Authorship Attribution 10. Clustering News Articles 11. Object Detection in Images using Deep Neural Networks 12. Working with Big Data 13. Next Steps...

Summary


In this chapter, we worked with images in order to use simple pixel values to predict the letter being portrayed in a CAPTCHA. Our CAPTCHAs were a bit simplified; we only used complete four-letter English words. In practice, the problem is much harder--as it should be! With some improvements, it would be possible to solve much harder CAPTCHAs with neural networks and a methodology similar to what we discussed. The scikit-image library contains lots of useful functions for extracting shapes from images, functions for improving contrast, and other image tools that will help.

We took our larger problem of predicting words, and created a smaller and simple problem of predicting letters. From here, we were able to create a feed-forward neural network to accurately predict which letter was in the image. At this stage, our results were very good with 97 percent accuracy.

Neural networks are simply connected sets of neurons, which are basic computation devices consisting of a single function...

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