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https://github.com/ugwaj/awesome-machine-learning-python

Machine and Deep Learning in Python
https://github.com/ugwaj/awesome-machine-learning-python

List: awesome-machine-learning-python

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Machine and Deep Learning in Python

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# Machine and Deep Learning with Python



## Business



* [Estimating a Real Business Cycle DSGE Model by Maximum Likelihood in Python](http://nbviewer.ipython.org/gist/ChadFulton/fbce8efd41fcf271b316)



## Bullying



* [Understanding and fighting bullying with machine learning](http://research.cs.wisc.edu/bullying)



## Gaming



* [Artificial intelligence learns Mario level in just 34 attempts](http://www.engadget.com/2015/06/17/super-mario-world-self-learning-ai)



## Recommendations 



* [Collaborative filtering recommendation engine implementation in python](http://dataaspirant.com/2015/05/25/collaborative-filtering-recommendation-engine-implementation-in-python)

* [NLP in python -- predicting HN upvotes from headlines](http://blog.dataquest.io/blog/predicting-upvotes)



## Text Analysis



* [Adam Palay - "Words, words, words": Reading Shakespeare with Python - PyCon 2015](https://www.youtube.com/watch?v=EoWG0lavg9U)

* [High-quality XML versions of the complete works of Shakespeare](https://github.com/severdia/PlayShakespeare.com-XML)

* [The Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)

* [Document Clustering with Python](http://nbviewer.ipython.org/github/brandomr/document_cluster/blob/master/cluster_analysis_web.ipynb)



## Money 



* [Predicting Heavy and Extreme Losses in Real-Time for Portfolio Holders] (http://www.quantatrisk.com/2015/06/14/predicting-heavy-extreme-losses-portfolio-1)



## Visualization



### Pyplot



* [Pyplot tutorial](http://matplotlib.org/users/pyplot_tutorial.html)

* [Plotly for IPython Notebooks](https://dato.com/learn/gallery/notebooks/food_retrieval-public.html)



## Deep Learning Frameworks



* [NErvana's pythON based Deep Learning Framework](https://github.com/NervanaSystems/neon)



## Video Streaming 



### Use Case Examples



* [Target acquired: Finding targets in drone and quadcopter video streams using Python and OpenCV](http://www.pyimagesearch.com/2015/05/04/target-acquired-finding-targets-in-drone-and-quadcopter-video-streams-using-python-and-opencv)

* [Visualization of taxi trip end points](https://www.kaggle.com/hochthom/pkdd-15-predict-taxi-service-trajectory-i/visualization-of-taxi-trip-end-points)

* [Basic motion detection and tracking with Python and OpenCV](http://www.pyimagesearch.com/2015/05/25/basic-motion-detection-and-tracking-with-python-and-opencv)

* [Home surveillance and motion detection with the Raspberry Pi, Python, OpenCV, and Dropbox](http://www.pyimagesearch.com/2015/06/01/home-surveillance-and-motion-detection-with-the-raspberry-pi-python-and-opencv)



## Time



* [Making Space Time Predictions using Python and Spark](https://www.youtube.com/watch?v=0YTIOn7_h_k)



## Audio



* [Classifying and Visualizing Musical Pitch with K-means Clustering](http://www.galvanize.com/blog/2015/05/28/classifying-and-visualizing-musical-pitch-with-k-means-clustering)



## Learning Machine Learning



* [Supervised learning superstitions cheat sheet](http://ryancompton.net/assets/ml_cheat_sheet/supervised_learning.html)

* [Introduction to Deep Learning with Python](https://www.youtube.com/watch?v=S75EdAcXHKk)

* [How to implement a neural network](http://peterroelants.github.io/posts/neural_network_implementation_part01)

* [How to build and run your first deep learning network]

(https://beta.oreilly.com/learning/how-to-build-and-run-your-first-deep-learning-network)

* [Neural Nets for Newbies by Melanie Warrick](https://www.youtube.com/watch?v=Cu6A96TUy_o)

* [Data Science 101: Interactive Analysis with Jupyter, Pandas and Treasure Data](http://blog.treasuredata.com/blog/2015/06/23/data-science-101-interactive-analysis-with-jupyter-pandas-and-treasure-data)

* [Deep Learning Tutorial](http://videolectures.net/kdd2014_salakhutdinov_deep_learning)



### Material Databases



* [Materials for Learning Machine Learning](http://www.jacksimpson.co/2015/06/07/materials-for-learning-machine-learning)

* [On Deep Learning

A Tweeted Bibliography](https://medium.com/deep-learning-101/on-deep-learning-a-tweeted-bibliography-68ab095376e7)

* [Continually updated Data Science Python Notebooks](https://github.com/donnemartin/data-science-ipython-notebooks)

* [http://people.duke.edu/~ccc14/sta-663/index.html](http://people.duke.edu/~ccc14/sta-663/index.html) 

* [Stanford Reports for 2015](http://cs224d.stanford.edu/reports.html)

* [Data Science Specialization](http://datasciencespecialization.github.io)

* [Unsupervised Feature Learning and Deep Learning](http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=ufldl)

* [Awesome Deep Vistion](https://github.com/szwed/awesome-deep-vision)



#### Cheatsheets



* [8 Best Machine Learning Cheat Sheets](http://designimag.com/best-machine-learning-cheat-sheets)



### Courses



* [Deep Learning Lecture - University of Oxford](http://www.computervisiontalks.com/tag/deep-learning-course/)



# Theory



## Articles



* [Machine Learning and Law - Harry Surden](http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2417415)

* [eBrevia Applies Machine Learning To Contract Review](http://www.forbes.com/sites/benkepes/2015/02/20/ebrevia-applies-machine-learning-to-contract-review/)

* [Introduction to Neural Machine Translation with GPUs](http://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-with-gpus)



## Alghoritms



### Fraud Detection



* [Detecting Fraudulent Personalities in Networks of Online Auctioneers](http://www.cs.cmu.edu/~dchau/papers/auction_fraud_pkdd06.pdf)



### Chat



* [A Neural Conversational Model](http://arxiv.org/pdf/1506.05869v1.pdf)



### Sport



* [Prediction and Quantification of Individual Athletic Performance](http://arxiv.org/pdf/1505.01147v2.pdf)



### Image Recognition



* [Generative Image Modeling Using Spatial LSTMs](http://arxiv.org/pdf/1506.03478v1.pdf)

* [Suddenly, a leopard print sofa appears](http://rocknrollnerd.github.io/ml/2015/05/27/leopard-sofa.html)

### Random



* [On the accuracy of self-normalized log-linear models](http://arxiv.org/pdf/1506.04147v1.pdf)

* [Bayesian Dark Knowledge](http://arxiv.org/pdf/1506.04416v1.pdf)



# Amazon AWS



## Lambda



* [The future is now, and it's using AWS Lambda](http://lg.io/2015/05/16/the-future-is-now-and-its-using-aws-lambda.html)



# Propaganda



## Short Articles



* [Machines that think for themselves](http://www.work.caltech.edu/paper/sciam2012.pdf)

* [How Artificial Intelligence Will Make Technology Disappear](https://medium.com/using-artificial-intelligence-to-make-technology/how-artificial-intelligence-will-make-technology-disappear-503cd88e1e6a)

* [Deep Learning Machine Beats Humans in IQ Test](http://www.technologyreview.com/view/538431/deep-learning-machine-beats-humans-in-iq-test/)



## Image recognition



* [What’s in This Picture? AI Becomes as Smart as a Toddler](http://www.bloomberg.com/news/articles/2015-05-22/what-s-in-this-picture-ai-becomes-as-smart-as-a-toddler)

* [Bringing Deep Learning to the Grocery Store](https://dato.com/learn/gallery/notebooks/food_retrieval-public.html)

* [PyImageSearch and Computer Vision] (http://www.talkpythontome.com/episodes/show/11/pyimagesearch-and-computer-vision)



## Python & Machine Learning



* [Python-Powered Machine Learning in the Cloud](http://www.pyvideo.org/video/3556/python-powered-machine-learning-in-the-cloud)



## Videos



### Generic



* [Humans Need Not Apply](https://www.youtube.com/watch?t=490&v=7Pq-S557XQU)



### Law



* [Professor Harry Surden Discusses Machine Learning within Law](https://www.youtube.com/watch?v=sOLXOsiX0Qk)



### Social Risks



* [Robot Economics] (https://www.youtube.com/watch?v=QGxH35SKInM)



## Tools 



### Libraries



* [A library to build and test machine learning features] (https://pypi.python.org/pypi/featureforge/0.1.6)

* [deepy: Highly extensible deep learning framework based on Theano](https://github.com/uaca/deepy)