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README.md

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* **Probabilistic Modeling**
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* Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](http://bit.ly/ipnb-probabilisticprogramming)
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* Probabalistic Graphical Models [Stanford / Coursera](http://bit.ly/stanford-pgm)
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* Probabilistic Graphical Models [Stanford / Coursera](http://bit.ly/stanford-pgm)
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* **Deep Learning (Neural Networks)**
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* Neural Networks [Andrej Karpathy / Python Walkthrough](http://karpathy.github.io/neuralnets/)
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* **Natural Language Processing & Understanding**
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* [NLTK](http://bit.ly/py-nltk) - Natural Language Toolkit
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* [Gensim](http://bit.ly/py-gensim) - Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.
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* [Gensim](http://bit.ly/py-gensim) - Python library for topic modeling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.
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* **Live Data Packages**
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* [twython](http://bit.ly/py-twython) - Python wrapper for the Twitter API

basic-programming.md

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* Python the Hard Way [Online Book & Tutorial](http://learnpythonthehardway.org/book/) - my personal favorite Pythonic starting point.
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#### **Startups and Software Engineering**
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* Startup Engineering [Stanford / Coursera](https://class.coursera.org/startup-001) _NB: This is a full-stack class; explains development from conception to deployment. Great granualar, stepwise course explaining how to built an application from scratch._
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* Startup Engineering [Stanford / Coursera](https://class.coursera.org/startup-001) _NB: This is a full-stack class; explains development from conception to deployment. Great granular, stepwise course explaining how to built an application from scratch._
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* [How Browsers Work](http://www.html5rocks.com/en/tutorials/internals/howbrowserswork/)
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### Programming Tools & key Python Resources

janitorial.md

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#### **Tools**
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* [CSV Fingerprints](http://setosa.io/blog/2014/08/03/csv-fingerprints/) makes it easy to spot irrregularities in a CSV visually.
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* [CSV Fingerprints](http://setosa.io/blog/2014/08/03/csv-fingerprints/) makes it easy to spot irregularities in a CSV visually.

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