This repo is a compilation of flash cards summarizing machine learning concepts, algorithms, math formulas, and more. The flash cards are implemented in Jupyter notebooks and formated to support easy conversion to Jupyter slides.

I have created them as an aid for delivering workshops, hackathons, and deep dive presentations.

The flash cards are excerpts and paraphrases taken from the following reading list. It is highly recommended to refer to these publications for detailed explanations.

Sources:

1. Kevin P. Murphy (2012). Machine Learning. A Probabilistic Perspective.
2. Christopher Bishop (2006). Pattern Recognition and Machine Learning.
3. Yaser Abu-Mostafa et al. (2012). Learning From Data.
4. Trevor Hastie et al. (2008). The Elements of Statistical Learning.
5. Tom M. Mitchell (1997). Machine Learning.
6. Ian Goodfellow, Yoshoua Bengio, Aaron Courville (2016). Deep Learning.
7. Dimitri P. Bertsekas, John N. Tsitsiklis (2008). Introduction to Probability
8. Sebastian Raschka et. al. (2017). Python Machine Learning

Since Github can be finicky with rendering Jupyter notebooks, especially ones with extensive LaTex content, it is recommend to clone the repo and render the notebooks in your local Jupyter runtime.