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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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Author (1):
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Robert Layton Robert Layton
Author Profile Icon Robert Layton
Robert Layton
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Table of Contents (20) Chapters Close

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
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...

Chapter 11. Object Detection in Images using Deep Neural Networks

We used basic neural networks in Chapter 8, Beating CAPTCHAs with Neural Networks with Neural Networks. Research in neural networks is creating some of the most advanced and accurate classification algorithms in many areas. The differences between the concepts introduced in this chapter, versus those introduced in Chapter 8, Beating CAPTCHAs with Neural Networks is around complexity. In this chapter, we look at deep neural networks, those with many hidden layers, and also at more complex layer types for dealing with specific types of information, such as images.

These advances have come on the back of improvements in computational power, allowing us to train larger and more complex networks. However, the advances are much more than simply throwing more computational power at the problem. New algorithms and layer types have drastically improved performance, outside computational power. The cost is that these new classifiers...

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