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Created June 27, 2017 16:19
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Notes and Resource from Machine Learning Independent Study
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machine-learning.md

Machine Learning Resources

Background Reading from Patrick

Udacity course on ML in Python from Clint

https://www.udacity.com/course/intro-to-machine-learning--ud120

Oxford course on Machine Learning

https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/

Focuses on neural networks. This first two lectures are a great introduction to deep learning concepts and the second lecture in particular provides a list of background knowledge you should have.

Khan Academy series on Linear Algebra

Straightforward lectures explaining basic linear algebra concepts like matricies, vectors and scalar values.

https://www.youtube.com/watch?v=xyAuNHPsq-g&list=PLFD0EB975BA0CC1E0

Video Series on Statistics in R

These videos were helpful for understanding how to use data when training models, specifically how to think about the way your construction of input will influence your output.

Video Series on Neural Networks

Explains the underlying calculus through examples of Neural Networks in Python.

https://www.youtube.com/watch?v=bxe2T-V8XRs

Implementing Explicit Content Detection

Hacker Noon series on Machine Learning with Javascript

Only parts 1 and 2 are out so far, but they are interesting and being able to use JS instead of Python let me get started much much faster.

https://hackernoon.com/machine-learning-with-javascript-part-1-9b97f3ed4fe5

NLP in Javascript

There's an entire course based on using Natural for NLP. Clint found this, but I have not taken the course yet. https://egghead.io/courses/natural-language-processing-in-javascript-with-natural

Unified and Retext for text processing

Retext plugins for detecting profanity, gendered lanuage and sentiment

Random

TWSS

When reseaching how to characterize suggestive language or innudendo rather than explicit language or profanity, I found this paper about identifying double entendre: http://people.cs.umass.edu/~brun/pubs/pubs/Kiddon11.pdf

It is implemented in this Ruby package with has an entertaining set of training data: https://github.com/sengupta/twss/blob/master/twss/positive.txt

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