Going In Deep On Data | YC Paper Club

Why it's on BrighterTube

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

YC Paper Club deep dive on data infrastructure and its role in AI. Technical paper discussion on systems design.

What you'll learn

A 53-minute video from Y Combinator: “Going In Deep On Data | YC Paper Club”.

Science & Math Programming & CS

Video by Y Combinator, played through YouTube's embedded player.

Part of Paper Club

1:00:24

Building Dota Bots That Beat Pros - OpenAI's Greg Brockman, Szymon Sidor, and Sam Altman Y CombinatorScience & Math

1:00:11

Why The Harness Matters More Than The Model | YC Paper Club Y CombinatorScience & 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

The Most Underrated Algorithm VeritasiumProgramming & CS

10.7M views

1:13:50

The Continuity of Splines Freya HolmérProgramming & CS

1.89M views

8:38

Diffie-Hellman Key Exchange Art of the ProblemProgramming & CS

938K views

Coding Adventure: Simulating Fluids Sebastian LagueProgramming & CS

2.66M views

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

Advanced177K views

JPEG DCT, Discrete Cosine Transform (JPEG Pt2)- Computerphile ComputerphileProgramming & CS

697K views

Category Theory 1.1: Motivation and Philosophy Bartosz MilewskiProgramming & CS

394K views

1:00:24

Building Dota Bots That Beat Pros - OpenAI's Greg Brockman, Szymon Sidor, and Sam Altman

Y Combinator

A 60-minute video from Y Combinator: “Building Dota Bots That Beat Pros - OpenAI's Greg Brockman, Szymon Sidor, and Sam Altman”.

1:00:11

Why The Harness Matters More Than The Model | YC Paper Club

Y Combinator

A 60-minute video from Y Combinator: “Why The Harness Matters More Than The Model | YC Paper Club”.

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.

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.

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.

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.

Coding Adventure: Simulating Fluids

Sebastian Lague

2.66M views

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

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.

JPEG DCT, Discrete Cosine Transform (JPEG Pt2)- Computerphile

Computerphile

697K views

Computerphile explains the discrete cosine transform at the heart of JPEG compression.

Category Theory 1.1: Motivation and Philosophy

Bartosz Milewski

394K views

The first lecture of Bartosz Milewski's Category Theory for Programmers: why abstraction and composition matter, and what category theory offers programmers.