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 . . .