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. 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.

(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

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 . . .