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Some python updates from @machinalis repos

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Daniel F Moisset 2014-07-27 12:11:22 -03:00
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@ -466,20 +466,24 @@ on MNIST digits[DEEP LEARNING]
* [NLTK](http://www.nltk.org/) - A leading platform for building Python programs to work with human language data.
* [Pattern](http://www.clips.ua.ac.be/pattern) - A web mining module for the Python programming language. It has tools for natural language processing, machine learning, among others.
* [Quepy](https://github.com/machinalis/quepy) - A python framework to transform natural language questions to queries in a database query language
* [TextBlob](http://textblob.readthedocs.org/) - Providing a consistent API for diving into common natural language processing (NLP) tasks. Stands on the giant shoulders of NLTK and Pattern, and plays nicely with both.
* [jieba](https://github.com/fxsjy/jieba#jieba-1) - Chinese Words Segmentation Utilities.
* [YAlign](https://github.com/machinalis/yalign) - A sentence aligner, a friendly tool for extracting parallel sentences from comparable * [jieba](https://github.com/fxsjy/jieba#jieba-1) - Chinese Words Segmentation Utilities.
* [SnowNLP](https://github.com/isnowfy/snownlp) - A library for processing Chinese text.
* [loso](https://github.com/victorlin/loso) - Another Chinese segmentation library.
* [genius](https://github.com/duanhongyi/genius) - A Chinese segment base on Conditional Random Field.
* [nut](https://github.com/pprett/nut) - Natural language Understanding Toolkit
* [Rosetta](https://github.com/columbia-applied-data-science/rosetta) - Text processing tools and wrappers (e.g. Vowpal Wabbit)
corpora.
<a name="python-general-purpose" />
#### General-Purpose Machine Learning
* [Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) - Book/iPython notebooks on Probabilistic Programming in Python
* [Featureforge](https://github.com/machinalis/featureforge) A set of tools for creating and testing machine learning features, with a scikit-learn compatible API
* [MLlib in Apache Spark](http://spark.apache.org/docs/latest/mllib-guide.html) - Distributed machine learning library in Spark
* [scikit-learn](http://scikit-learn.org/) - A Python module for machine learning built on top of SciPy.
* [SimpleAI](http://github.com/simpleai-team/simpleai) Python implementation of many of the artificial intelligence algorithms described on the book "Artificial Intelligence, a Modern Approach". It focuses on providing an easy to use, well documented and tested library.
* [astroML](http://www.astroml.org/) - Machine Learning and Data Mining for Astronomy.
* [graphlab-create](http://graphlab.com/products/create/docs/) - A library with various machine learning models (regression, clustering, recommender systems, graph analytics, etc.) implemented on top of a disk-backed DataFrame.
* [BigML](https://bigml.com) - A library that contacts external servers.