Excellent Technical Resources

Created on May 6, 2021

Updates to this list are made with a degree of care somewhere between pedantic and perfunctory. With that said, each resource listed here has provided me with invaluable technical skill and I would encourage others to peruse them. In Sir Isaac Newton’s words: “If I have seen further it is by standing on the shoulders of giants.”


  • Of the myriad Intro to Programming courses available online, the “Pythonic” Intro to Data Structures and Algorithms by Grow with Google on Udacity is one of my favorites due to its concision, structure, and price (it’s free).

  • CS 61B is a course offered by EECS at UC Berkeley and taught by Professor Josh Hug. Attempting to master the contents of this course will distance you from the CS charlatan. Revise OOP, recursion, lists, and trees prior to diving in.


  • Learning from Data is designed for a “short course, not a hurried course” on machine learning. Experienced professors from Caltech, RPI, and NTU authored this book based on what they believe to be the core topics that every student of the subject should know.

  • Use Appendix A in Convex Optimization by Boyd and Vandenberghe (EE professors at Stanford and UCLA respectively) as a refresher of some basic concepts from analysis and linear algebra.

  • The Matrix Cookbook is essentially a matrix calculus cheatsheet, i.e., don’t expect lengthy proofs and satisfying explanations. Simply use the given formulas to get on with improving your ML model(s).


  • I am enamored with TeX. My website uses the Computer Modern family of typefaces developed by Donald Knuth (Stanford) for TeX.

    • This link points to a LaTeX style guide for a convex optimization course at Stanford. It includes tips not only related to LaTeX but also academic writing in general.