Youtube #

playlists #

• MIT 18.06 Linear Algebra, Prof. Gilbert Strang, MIT, link
• MIT 18.065 Matrix Methods In Data Analysis, Signal Processing, and Machine Learning, Prof. Gilbert Strang, MIT, link
• Math 131 Real Analysis, Prof. Francis Su, Harvey Mudd College link
• EE364A Convex Optimization, Prof. Stephen Boyd, Stanford University, link

videos #

• Grant Sanderson, Harvey Mudd Commencement Speech 2024, link
• Alan Edelman on Julia, at TEDxMIT 2020, link
• Git Internals by John Britton of GitHub, CS50 Tech Talk, link
• Advice from the Top 1% of Software Engineer, Jean Lee, link

channels #

• MIT OpenCourseWare, link

Lots of lecture series. Free knowledge!

• Stanford Online, link

Same as above.

• 3Blue1Brown, link

Explanation of math-y concepts with pretty animations.

• Fireship, link

Latest tech news (gossip).

Books #

• Introduction to Linear Optimization, Dimitris Bertsimas, John N. Tsitsiklis, link

• Differential Equations with Applications and Historical Notes, George F. Simmons, link

• Linear Algebra and Learning from Data, Gilbert Strang, link

Articles #

• Understanding Logistic Regression, Arun Addagatla, link

• Maximum Likelihood Estimation in Logistic Regression, Arun Addagatla, link

• Support Vector Machines — Soft Margin Formulation and Kernel Trick, Rishabh Misra, link

• Activation Functions Demystified, Om Pramod, link

• Batch Norm Explained Visually — How it works, and why neural networks need it, Ketan Doshi, link

• Inspecting Layer Normalization In Transformers, Ryan Partidge, link

• What is Memoization?, Geeks for geeks, link

• The Math Behind "The Curse of Dimensionality", Maxime Wolf, link