Introduction to the branches of mathematics used in machine learning. Linear algebra, statistics, calculus.
## Resources
Come back here after you've finished Ng's course; or learn these resources in tandem with ML (say 1 day a week).
Primers (PDFs)
- See "Section Notes" of cs229 (http://cs229.stanford.edu/materials.html) `handout:medium`
KhanAcademy:
- Either LinAlg (https://www.khanacademy.org/math/linear-algebra) `course:medium` OR Fast.ai (http://www.fast.ai/2017/07/17/num-lin-alg/) `course:medium`
- Stats (https://www.khanacademy.org/math/statistics-probability) `course:medium`
- Calc (https://www.khanacademy.org/math/calculus-home) `course:medium`
Books
- Introduction to Linear Algebra (https://amzn.to/2wP8TWS) `book:hard`
- All of statistics (http://amzn.to/2t2dOwg) `book:hard`
- Calculus (http://amzn.to/2tXfXhp) `book:hard`
Audio (supplementary material)
- Statistics (https://goo.gl/4vvXJs), Probability (https://goo.gl/Q4KwZ6) `audio|course:hard`
- Calculus 1 (https://goo.gl/fcLP3l), 2 (https://goo.gl/sBpljN), 3 (https://goo.gl/8Hdwuh) `audio|course:hard`
- Mathematical Decision Making (https://goo.gl/V75I49) `audio|course:hard` course on "Operations Research", similar to ML
- Information Theory (https://goo.gl/ugAi2m) `audio|course:hard`
- Convert video to audio:
** mp4 => mp3: `for f in *.mp4; do ffmpeg -i "$f" "${f%.mp4}.mp3" && rm "$f"; done`
** youtube => mp3: setup youtube-dl (https://github.com/rg3/youtube-dl) and run `youtube-dl -x youtube.com/playlist?list=
## Episode
- Linear Algebra = Matrix (or "Tensor") math. Wx + b. Chopping in our analogy.
- Stats = Probability/inference, the heart of machine learning. Recipes/cookbook.
- Calculus = Learning. Moving our error dot to the bottom of the valley. Baking, the actual "cook" step.
