Ranking ważności atrybutów w narzędziu RapidMiner

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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.

What you'll learn

A 18-minute video from Mikołaj Morzy: “Ranking ważności atrybutów w narzędziu RapidMiner”.

Programming & CS

Video by Mikołaj Morzy, played through YouTube's embedded player.

17:58

Wprowadzenie do narzędzia Orange Data Mining Mikołaj MorzyProgramming & CS

Intro

27:11

Wprowadzenie do algorytmu SVM Mikołaj MorzyProgramming & CS

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

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

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Transformers, the tech behind LLMs | Deep Learning Chapter 5 3Blue1BrownProgramming & CS

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24:26

The Beauty of Bézier Curves Freya HolmérProgramming & CS

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2:04:55

CS50x 2024 - Lecture 0 - Scratch CS50Programming & CS

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2:27:41

CS50x 2024 - Lecture 1 - C CS50Programming & CS

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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

17:58

Wprowadzenie do narzędzia Orange Data Mining

Mikołaj Morzy

Intro

A 18-minute video from Mikołaj Morzy: “Wprowadzenie do narzędzia Orange Data Mining”.

27:11

Wprowadzenie do algorytmu SVM

Mikołaj Morzy

Intro

A 27-minute video from Mikołaj Morzy: “Wprowadzenie do algorytmu SVM”.

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.