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 . . .