STOC 2020 - Session 10B: Complexity Theory III

Association for Computing Machinery (ACM)49.8K subscribers

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

Complexity theory session covering computational bounds and hardness results. Teaching theoretical foundations of problem difficulty and computational limitations.

What you'll learn

A 39-minute video from Association for Computing Machinery (ACM): “STOC 2020 - Session 10B: Complexity Theory III”.

Programming & CS Science & Math

Video by Association for Computing Machinery (ACM), played through YouTube's embedded player.

Part of STOC 2020 Live Sessions

But what is a neural network? | Deep learning chapter 1 3Blue1BrownScience & Math25.1M views

Transformers, the tech behind LLMs | Deep Learning Chapter 5 3Blue1BrownProgramming & CS11.6M views

24:26

The Beauty of Bézier Curves Freya HolmérProgramming & CS2.23M views

1:13:50

The Continuity of Splines Freya HolmérProgramming & CS1.89M views

The Most Underrated Algorithm VeritasiumProgramming & CS10.7M views

Coding Adventure: Simulating Fluids Sebastian LagueProgramming & CS2.66M views

8:38

Diffie-Hellman Key Exchange Art of the ProblemProgramming & CS938K views

A Better Default Colormap for Matplotlib | SciPy 2015 | Nathaniel Smith and Stéfan van der Walt EnthoughtProgramming & CS177K views

16:31

Public Key Cryptography: RSA Encryption Art of the ProblemProgramming & CS996K views

JPEG DCT, Discrete Cosine Transform (JPEG Pt2)- Computerphile ComputerphileProgramming & CS697K views

Category Theory 1.1: Motivation and Philosophy Bartosz MilewskiProgramming & CS394K views

44:23

How Hard is too Hard? An Introduction to Complexity - Colva Roney-Dougal Gresham CollegeLectures8.82K views

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

3Blue1Brown25.1M 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

3Blue1Brown11.6M 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 Holmér2.23M 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 Holmér1.89M 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

Veritasium10.7M views

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

Coding Adventure: Simulating Fluids

Sebastian Lague2.66M views

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

8:38

Diffie-Hellman Key Exchange

Art of the Problem938K 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

Enthought177K 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

44:23

How Hard is too Hard? An Introduction to Complexity - Colva Roney-Dougal

Gresham College8.82K views

Colva Roney-Dougal introduces computational complexity: what makes problems hard, P vs NP and why it matters.