function composition
14:28Superpositions, Sudoku, the Wave Function Collapse algorithm.
764K views • 6 years ago
Martin Donald
Martin Donald explains the Wave Function Collapse algorithm using Sudoku-like constraint solving and shows how he implemented it with Blender and Godot. It links demos and reference articles.
22:59How Does SHA-256 Work?
374K views • 6 years ago
learnmeabitcoin
A step-by-step animated explanation of how SHA-256 works, from padding and message schedule to the compression function and final hash, with source code for the animations.
26:41Finding the BEST sine function for Nintendo 64
418K views • 3 years ago
Kaze Emanuar
Kaze Emanuar searches for the best sine approximation for the Nintendo 64, comparing the lookup tables in Mario 64 and Ocarina of Time with polynomial alternatives. It walks through numerics and 3rd, 4th and 5th order approximations.
55:02GradIEEEnt half decent: The hidden power of imprecise lines
391K views • 3 years ago
suckerpinch
Tom 7 (suckerpinch) delivers a long, comedic digression on gradient descent and floating-point imprecision. He shows how rounding behavior can supply the nonlinearity needed to build a neural network.
20:00Why Parallel Universes Are Probably Real
25.4M views • 6 years ago
Veritasium
Veritasium explains the many-worlds interpretation of quantum mechanics, drawing heavily on Sean Carroll's book. It walks through the Schrodinger equation and why wave collapse may be unnecessary if the universe keeps splitting.
45:55Turning Waves Into Particles
194K views • 1 year ago
Huygens Optics
Huygens Optics explores the idea that space is a non-linear elastic medium, and uses simulations to show how wave energy might be confined locally into particle-like objects. It touches on inertia, the Schwinger limit and the vacuum as a medium.
8:25Large Language Models from scratch
375K views • 4 years ago
Graphics in 5 Minutes
A short cartoon explaining how language models like ChatGPT work, from autocomplete and conditional probabilities to neural networks, gradient descent and backpropagation. It shows how networks act as universal function approximators.