All of the sources listed below are from material that I have learned from. Whether these are readings, videos, audios, or other, they have contributed to my knowledge basis and I believe I have a moral obligation to credit them. The sources below are in no particular order. There is no particular date on when I began collecting these. I sometimes forget to write them down. This is what I tend to keep.
MIT 18.01 Single Variable Calculus
Learn & Practice Resources for Calculus
Computational Linear Algebra for Coders
MIT 18.06 Linear Algebra (Prof. Gilbert Strang)
Combinatorics and Graph Theory
An Introduction to Combinatorics and Graph Theory
Graph Theory with Applications
Chris Bourke Algorithms & Algorithm Analysis
Mathematics for Computer Science, 6.042J
How to Think Like a Mathematician
Introduction to Probability, Statistics, and Random Processes
Mathematics for Machine Learning — Ulrike von Luxburg, 2020/21
AWS Machine Learning Skill Builder
Pattern Recognition and Machine Learning
Machine Learning for Intelligent Systems
Machine Learning, A Probabilistic Perspective
Data Driven Science & Engineering
Data Science from Scratch, First Principles with Python
Mathematics for Machine Learning Book
Find Free Courses Online (Class Central)
Detexify (Draw Symbol -> LaTeX)
6.0001 Introduction to Computer Science
CS61B UC Berkeley Data Structures
MIT Introduction To Algorithms, Third Edition (CLRS)
Analysis of Algorithms (Skiena)
Example User Schema Properties
Password Storage Cheat Sheet (OWASP)
MIT Version Control (Missing Semester)
The Rust Programming Language Book
Journal of Machine Learning Research (JMLR)
r/REU (Research Experiences for Undergraduates)
List of Companies Hiring Without "Whiteboard" Interviews
SQL Interview Questions (InterviewBit)
LeetCode Interview Questions for Big Tech (FLAG)
Technical Interview Preparation Guide
Preparing for your Interview (Meta Careers)
Company Interview Questions (GitHub Repo)
Beginner's Guide to Deploying Web Apps
Synthesia (AI Video Generation)
WolframAlpha (Computational Knowledge Engine)
Online Compiler (Many Languages)
Computer Vision: Algorithms and Applications, 2nd Edition
The Missing Semester of Your CS Education
Linear Programming Introduction
A Guide to Technical Interviews for AI Researchers (PDF) — March 2025
Cracking the Coding Interview (6th Edition) discussion on LeetCode
Deep Learning Interview Questions list by Julian8897 on Medium
Hugging Face docs: Accelerate — FSDP and DeepSpeed
Machine Learning Cheat Sheet PDF (by SoulMachine)
Machine Learning Interview (GitHub) by khangich
Spinning Up in Deep Reinforcement Learning (OpenAI)
Symmetric Key Cryptanalysis How-To by akircanski
The Novice's LLM Training Guide
Top 55 Machine Learning Interview Questions for 2025
How the backpropagation algorithm works
Deep Learning in Neural Networks: An Overview
An Introduction to Convolutional Neural Networks
Recurrent Neural Networks (RNNs): A gentle Introduction and Overview
Steps Toward Artificial Intelligence
A visual proof that neural nets can compute any function
Lecture 2 | The Universal Approximation Theorem
New Video Tutorial: Make a Neural Net Simulator in C++
If my kids excel, will they move away?
Duke University Introduction to Machine Learning
Technical Interivew Guide for AI Researchers & ML Engineers
Introduction to Machine Learning Interviews
Lecture 1: Understanding Machine Learning Production [draft]
Analysis of large binaries and games in Ghidra-SRE
Reversing WannaCry Part 3 - The encryption component
Reversing WannaCry Part 1 - Finding the killswitch and unpacking the malware in #Ghidra
WANNACRY: Earth's Deadliest [Computer] Viruses
GitHub - NationalSecurityAgency/ghidra
pwn.college - Program Security - Reverse Engineering
pwn.college - System Security - Kernel Security
pwn.college - Intro to Cybersecurity - Intercepting Communication
pwn.college - Assembly Refresher - Computer Architecture
pwn.college - Program Interaction - Linux Process Loading
pwn.college - Program Interaction - Binary Files
pwn.college - Program Interaction - Linux Process Loading
pwn.college - Program Interaction - Linux Process Execution
pwn.college - Assembly Refresher - Assembly
In-depth: ELF - The Extensible & Linkable Format
Using GDB to look at core files
Basics of Probability: Unions, Intersections, and Complements
An Introduction to Conditional Probability
Estimation Approximation Errors
The Complete Mathematics of Neural Networks and Deep Learning by Adam Dhalla
Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig
Neural Networks, University of Pennsylvania
Math animations derivatives by noureldin hassan
The Jacobian Matrix by Christopher Lum
Introduction to Neural Networks by Christopher Lum
Introduction to Neural Networks by Christopher Lum
"Surely You're Joking, Mr. Feynman!"
Tips for Writing Technical Papers
Neural Networks and Deep Learning - Chapter 1
Deep Learning State of the Art (2018) | MIT
A Gentle Introduction to Homological Algebra
Formal Mathematics Statement Curriculum Learning
Neural Networks - Mathematical Tours
Why Mini-batch Size is Better (Stack Exchange)
Neural Networks and Deep Learning - Chapter 2