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Learning Data Mining with Python

You're reading from   Learning Data Mining with Python Harness the power of Python to analyze data and create insightful predictive models

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Product type Paperback
Published in Jul 2015
Publisher Packt
ISBN-13 9781784396053
Length 344 pages
Edition 1st Edition
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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 (15) 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. Extracting Features with Transformers 6. Social Media Insight Using Naive Bayes 7. Discovering Accounts to Follow Using Graph Mining 8. Beating CAPTCHAs with Neural Networks 9. Authorship Attribution 10. Clustering News Articles 11. Classifying Objects in Images Using Deep Learning 12. Working with Big Data A. Next Steps… Index

Chapter 11: Classifying Objects in Images Using Deep Learning

Keras and Pylearn2

Other deep learning libraries that are worth looking at, if you are going further with deep learning in Python, are Keras and Pylearn2. They are both based on Theano and have different usages and features.

Keras can be found here: https://github.com/fchollet/keras/.

Pylearn2 can be found here: http://deeplearning.net/software/pylearn2/.

Both are not stable platforms at the time of writing, although Pylearn2 is the more stable of the two. That said, they both do what they do very well and are worth investigating for future projects.

Another library called Torch is very popular but, at the time of writing, it doesn't have python bindings (see http://torch.ch/).

Mahotas

Another package for image processing is Mahotas, including better and more complex image processing techniques that can help achieve better accuracy, although they may come at a high computational cost. However, many image processing tasks are good...

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