Coding the matrix exponential on so(3)

About this video

Hand-picked by a BrighterTube curator · found via round-3 back catalogue of a channel Lumen picks video by video · 82% confidence

Codes the matrix exponential on the Lie algebra so(3). Implements a key result in differential geometry and mechanics.

A 37-minute video from Nikolaj-K: “Coding the matrix exponential on so(3)”.

Science & Math Programming & CS

Video by Nikolaj-K, played through YouTube's embedded player.

21:42

Computing good rational approximations to any real Nikolaj-KProgramming & CS

36:27

Computing all polynomials via just ReLU activations (no learning needed), Part 1 of 2 Nikolaj-KScience & Math

But what is a neural network? | Deep learning chapter 1 3Blue1BrownScience & Math

Intro25.1M views

Transformers, the tech behind LLMs | Deep Learning Chapter 5 3Blue1BrownProgramming & CS

Intro11.6M views

24:26

The Beauty of Bézier Curves Freya HolmérProgramming & CS

2.23M views

1:13:50

The Continuity of Splines Freya HolmérProgramming & CS

1.89M views

The Most Underrated Algorithm VeritasiumProgramming & CS

10.7M views

30 Weird Chess Algorithms: Elo World suckerpinchProgramming & CS

965K views

Coding Adventure: Simulating Fluids Sebastian LagueProgramming & CS

2.66M views

8:38

Diffie-Hellman Key Exchange Art of the ProblemProgramming & CS

938K views

A Better Default Colormap for Matplotlib | SciPy 2015 | Nathaniel Smith and Stéfan van der Walt EnthoughtProgramming & CS

Advanced177K views

16:31

Public Key Cryptography: RSA Encryption Art of the ProblemProgramming & CS

996K views

21:42

Computing good rational approximations to any real

Nikolaj-K

A 22-minute video from Nikolaj-K: “Computing good rational approximations to any real”.

36:27

Computing all polynomials via just ReLU activations (no learning needed), Part 1 of 2

Nikolaj-K

A 36-minute video from Nikolaj-K: “Computing all polynomials via just ReLU activations (no learning needed), Part 1 of 2”.

But what is a neural network? | Deep learning chapter 1

3Blue1Brown

Intro25.1M views

Builds up what a neural network actually is — neurons as numbers, layers, weights and biases — using handwritten-digit recognition as the running example.

Transformers, the tech behind LLMs | Deep Learning Chapter 5

3Blue1Brown

Intro11.6M views

How transformer models (the 'T' in GPT) turn text into vectors and predict the next token, step by step.

24:26

The Beauty of Bézier Curves

Freya Holmér

2.23M views

Freya Holmér builds Bézier curves from linear interpolation up, covering de Casteljau's algorithm, derivatives, arc length and their use in games and graphics.

1:13:50

The Continuity of Splines

Freya Holmér

1.89M views

A follow-up on splines: the different kinds of continuity (C and G), and how Hermite, Catmull-Rom, B-splines and NURBS trade them off.

The Most Underrated Algorithm

Veritasium

10.7M views

The history and inner workings of the Fast Fourier Transform, from nuclear test detection to everyday signal processing.

30 Weird Chess Algorithms: Elo World

suckerpinch

965K views

Tom 7 pits 30 deliberately weird chess algorithms against each other and explains how Elo ratings work.

Coding Adventure: Simulating Fluids

Sebastian Lague

2.66M views

Building a fluid simulation step by step with smoothed-particle hydrodynamics.

8:38

Diffie-Hellman Key Exchange

Art of the Problem

938K views

Art of the Problem explains Diffie–Hellman key exchange with the paint-mixing analogy and then the modular-arithmetic version.

A Better Default Colormap for Matplotlib | SciPy 2015 | Nathaniel Smith and Stéfan van der Walt

Enthought

Advanced177K views

Nathaniel Smith and Stéfan van der Walt explain colour perception and perceptual uniformity, and how they designed viridis, Matplotlib's new default colormap.

16:31

Public Key Cryptography: RSA Encryption

Art of the Problem

996K views

How RSA public-key encryption works, from one-way functions through Euler's totient to the full algorithm.