# Explore My LaTeX Obsession

Here you’ll find my notes for various courses and textbooks I’ve read in my spare time. 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.

## (In Progress) Deep Learning Notes

Since graduating, I’ve been working through various textbooks/papers, compiling notes along the way. Originally this was just a single document, but it became too large to compile in TextStudio! Now it is comprised of the following sub-documents:

- Deep Learning by Goodfellow, et al.
- Speech and Language Processing by Jurafsky and Martin
- Other Textbooks
- Probabilistic Graphical Models by Koller and Friedman.
- Information Theory, Inference, and Learning Algorithms by Mackay.
- Machine Learning: A Probabilistic Perspective by Kevin Murphy.
- Bayesian Data Analysis by Gelman et al.

- Papers: A subset of my favorite papers that I frequently reference for my work.
- Misc concepts/clarifications on a variety of ML topics.

The full single document, before breaking up into the chunks above, can be reached by clicking the button below.

## Stanford Courses

In February 2019, I enrolled in the Artificial Intelligence Graduate Certificate program at Stanford University. I’ll be updating this section with my course notes at the end of each quarter.

- (CS221) Artificial Intelligence: Principles and Techniques
- (CS236) Deep Generative Models
- (CS330) Deep Multi-Task and Meta Learning
- (STATS214) Machine Learning Theory

## 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 . . .