# Explore My LaTeX Obsession

Here you’ll find my notes for the current (Fall 2016) semester. I have a passion for “making things look nice”. Many of the little customizations you’ll find in my notes are from LaTeX packages I’ve written. Each package is usually born in the hour-long google session of *why can’t I make it look like [insert trivial change here]*. These notes would not be possible without my inability to ignore a formatting issue. For even more fun, notice how the formatting in each set of notes improves as you progress through the chapters.

## (In Progress) Deep Learning Notes

Since graduating, I’ve been going through various books/MOOCs on deep learning, compiling notes along the way. The main sources are

- Deep Learning by Goodfellow, et al.
- Various ML/DL blogs, notably Colah’s blog and WildML.
- Miscellaneous papers from groups like DeepMind.

## Machine Learning

Here you’ll find notes from . . .

- UC Berkeley’s Machine Learning course (CS 189), taught by Professors Jitendra Malik and Benjamin Recht.
- Stanford’s (online) Machine Learning course materials (CS 229), taught by Professor Andrew Ng.
- The Elements of Statistical Learning by Hastie, et al.
- Professor Jonathan Shewchuck’s compilation of notes.

## Neural Computation

Here you’ll find notes from . . .

- UC Berkeley’s graduate course on Neural Computation (VS 265), taught by Professor Bruno Olhausen.
- Introduction to the Theory of Neural Computation by Hertz, et al.
- Deep Learning and Neural Networks by Heaton (AIFH series).

## Discrete Math and Probability

Here you’ll find notes from . . .

- UC Berkeley’s Discrete Mathematics and Probability course (CS70).
- Discrete Mathematics and Its Applications by Rosen.
- Selected notes and articles by UC Berkeley Professors and TAs.

## Analytic Mechanics

Here you’ll find notes from . . .

- UC Berkeley’s Analytic Mechanics course (PHYS 105).
- Classical Mechanics by John R. Taylor.
- Classical Dynamics by Thornton and Marion.

More notes coming soon . . .