Narzędzia AI w pracy z dźwiękiem i nie tylko

About this video

Editorial review · 85% confidence

Round-11 hunt: picked; channel-level screens (sellers/exam/re-upload/devotional/vendor/events), provenance check and vendor/seller/devotional recheck passed; strict per-title check + final strict pass; oEmbed verified.

A 68-minute video from Katedra Akustyki UAM: “Narzędzia AI w pracy z dźwiękiem i nie tylko”.

Programming & CS

Video by Katedra Akustyki UAM, played through YouTube's embedded player.

1:00:16

Generowanie mowy i synchronizacja z obrazem - Przemysław Ślużyński Katedra Akustyki UAMProgramming & CS

1:00:55

Sem KA & MTPR: Tomasz Grzywalski Learning to Listen Again: Cycles of Understanding in Artificial Int Katedra Akustyki UAMProgramming & CS

Advanced

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

Intro25.1M views

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

8.21M views

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

Intro4.41M 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

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

65.7K views

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

5.85M views

2:04:55

CS50x 2024 - Lecture 0 - Scratch CS50Programming & CS

Intro2.2M views

1:13:50

The Continuity of Splines Freya HolmérProgramming & CS

1.89M views

1:00:16

Generowanie mowy i synchronizacja z obrazem - Przemysław Ślużyński

Katedra Akustyki UAM

A 60-minute video from Katedra Akustyki UAM: “Generowanie mowy i synchronizacja z obrazem - Przemysław Ślużyński”.

1:00:55

Sem KA & MTPR: Tomasz Grzywalski Learning to Listen Again: Cycles of Understanding in Artificial Int

Katedra Akustyki UAM

Advanced

A 61-minute video from Katedra Akustyki UAM: “Sem KA & MTPR: Tomasz Grzywalski Learning to Listen Again: Cycles of Understanding in Artificial Int”.

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.

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'.

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.

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.

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.

“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'.

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.

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.