Computer Vision using DL. Лекция 6. Распознавание лиц (2019)

About this video

Editorial review · 85% confidence

Round-10 hunt: picked; channel-level screens (sellers/exam/re-upload/devotional/vendor/events) and provenance check passed; strict per-title check + final strict pass; oEmbed verified.

A 55-minute video from Евгений Разинков: “Computer Vision using DL. Лекция 6. Распознавание лиц (2019)”.

Programming & CS

Video by Евгений Разинков, played through YouTube's embedded player.

1:00:29

Обнаружение аномалий во временных рядах. Лекция 4 по обнаружению аномалий (2021)Евгений РазинковProgramming & CS

40:15

VLMs and Qwen 3, Understanding pre-training in Qwen3-VL Евгений РазинковProgramming & CS

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

2:04:55

CS50x 2024 - Lecture 0 - Scratch CS50Programming & CS

Intro2.2M 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

RailsConf 2014 - All the Little Things by Sandi Metz ConfreaksProgramming & CS

190K views

1:00:29

Обнаружение аномалий во временных рядах. Лекция 4 по обнаружению аномалий (2021)

Евгений Разинков

A 60-minute video from Евгений Разинков: “Обнаружение аномалий во временных рядах. Лекция 4 по обнаружению аномалий (2021)”.

40:15

VLMs and Qwen 3, Understanding pre-training in Qwen3-VL

Евгений Разинков

A 40-minute video from Евгений Разинков: “VLMs and Qwen 3, Understanding pre-training in Qwen3-VL”.

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.

2:04:55

CS50x 2024 - Lecture 0 - Scratch

CS50

Intro2.2M views

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

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'.

RailsConf 2014 - All the Little Things by Sandi Metz

Confreaks

190K views

Sandi Metz refactors the notorious Gilded Rose kata live, showing how small, disciplined steps and duplication-over-wrong-abstraction lead to clean object-oriented code.