Building data-parallel pipelines in Erlang: Pero Subasic

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

Teaches data-parallel pipeline construction in Erlang for scalable processing. A technical presentation demonstrating functional data processing patterns and parallelization techniques.

A 48-minute video from Erlang Solutions: “Building data-parallel pipelines in Erlang: Pero Subasic”.

Programming & CS

Video by Erlang Solutions, played through YouTube's embedded player.

48:43

Mark Allen - Before Unix: An Early History of Timesharing Systems Erlang SolutionsHistory

38:15

Accidentally Concurrent - Evan Czaplicki Erlang SolutionsProgramming & CS

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

Intro25.1M views

The spelled-out intro to neural networks and backpropagation: building micrograd Andrej KarpathyProgramming & CS

Intro4.41M views

Let's build GPT: from scratch, in code, spelled out.Andrej KarpathyProgramming & CS

8.21M 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

2:27:41

CS50x 2024 - Lecture 1 - C CS50Programming & CS

Intro1.41M views

2:04:55

CS50x 2024 - Lecture 0 - Scratch CS50Programming & CS

Intro2.2M views

“Hello, world” from scratch on a 6502 — Part 1 Ben EaterProgramming & CS

5.85M views

Ruby Conf 12 - Y Not- Adventures in Functional Programming by Jim Weirich ConfreaksProgramming & CS

65.7K views

1:13:50

The Continuity of Splines Freya HolmérProgramming & CS

1.89M views

48:43

Mark Allen - Before Unix: An Early History of Timesharing Systems

Erlang Solutions

A 49-minute video from Erlang Solutions: “Mark Allen - Before Unix: An Early History of Timesharing Systems”.

38:15

Accidentally Concurrent - Evan Czaplicki

Erlang Solutions

A 38-minute video from Erlang Solutions: “Accidentally Concurrent - Evan Czaplicki”.

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.

The spelled-out intro to neural networks and backpropagation: building micrograd

Andrej Karpathy

Intro4.41M views

Building micrograd, a tiny autograd engine, to understand backpropagation completely.

Let's build GPT: from scratch, in code, spelled out.

Andrej Karpathy

8.21M views

Building a GPT-style language model from scratch in code, following 'Attention Is All You Need'.

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.

2:27:41

CS50x 2024 - Lecture 1 - C

CS50

Intro1.41M views

CS50x 2024 lecture 1: an introduction to C — syntax, types, conditionals, loops and compiling.

2:04:55

CS50x 2024 - Lecture 0 - Scratch

CS50

Intro2.2M views

CS50x 2024 lecture 0: how computers represent information, algorithms, and first programs in Scratch.

“Hello, world” from scratch on a 6502 — Part 1

Ben Eater

5.85M views

Building a computer around the 6502 processor on a breadboard and running 'Hello, world'.

Ruby Conf 12 - Y Not- Adventures in Functional Programming by Jim Weirich

Confreaks

65.7K views

Jim Weirich derives the Y combinator live, step by step, using nothing but Ruby lambdas, and along the way teaches the refactorings of lambda calculus.

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.