03: Fortgeschrittene Datenstrukturen, Binomialbäume, Fortgeschrittene Graphenalgorithmen

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What you'll learn

A 75-minute video from KIT Lehre und Wissen: “03: Fortgeschrittene Datenstrukturen, Binomialbäume, Fortgeschrittene Graphenalgorithmen”.

Programming & CS

Video by KIT Lehre und Wissen, played through YouTube's embedded player.

1:26:45

Grundlagen der Informatik II - Sitzung 4 (Prof. Schmeck - AIFB - WS 2016/17)KIT Lehre und WissenProgramming & CS

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21UB+VL: Übung zu Entwurfsmustern und Fortsetzung Parallelität in Java Bis zu 4K KIT Lehre und WissenProgramming & CS

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Let's build GPT: from scratch, in code, spelled out.Andrej KarpathyProgramming & CS8.21M views

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The spelled-out intro to neural networks and backpropagation: building micrograd Andrej KarpathyProgramming & CS4.41M views

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

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CS50x 2024 - Lecture 1 - C CS50Programming & CS1.41M views

“Hello, world” from scratch on a 6502 — Part 1 Ben EaterProgramming & CS5.85M views

Ruby Conf 12 - Y Not- Adventures in Functional Programming by Jim Weirich ConfreaksProgramming & CS65.7K views

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CS50x 2024 - Lecture 0 - Scratch CS50Programming & CS2.2M views

RailsConf 2014 - All the Little Things by Sandi Metz ConfreaksProgramming & CS190K views

1:26:45

Grundlagen der Informatik II - Sitzung 4 (Prof. Schmeck - AIFB - WS 2016/17)

KIT Lehre und Wissen

A 87-minute video from KIT Lehre und Wissen: “Grundlagen der Informatik II - Sitzung 4 (Prof. Schmeck - AIFB - WS 2016/17)”.

1:28:21

21UB+VL: Übung zu Entwurfsmustern und Fortsetzung Parallelität in Java Bis zu 4K

KIT Lehre und Wissen

A 88-minute video from KIT Lehre und Wissen: “21UB+VL: Übung zu Entwurfsmustern und Fortsetzung Parallelität in Java Bis zu 4K”.

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.

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Andrej Karpathy8.21M views

Building a GPT-style language model from scratch in code, following 'Attention Is All You Need'.

Transformers, the tech behind LLMs | Deep Learning Chapter 5

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How transformer models (the 'T' in GPT) turn text into vectors and predict the next token, step by step.

The spelled-out intro to neural networks and backpropagation: building micrograd

Andrej Karpathy4.41M views

Building micrograd, a tiny autograd engine, to understand backpropagation completely.

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.

2:27:41

CS50x 2024 - Lecture 1 - C

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CS50x 2024 lecture 1: an introduction to C — syntax, types, conditionals, loops and compiling.

“Hello, world” from scratch on a 6502 — Part 1

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Building a computer around the 6502 processor on a breadboard and running 'Hello, world'.

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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.

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CS50x 2024 - Lecture 0 - Scratch

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

RailsConf 2014 - All the Little Things by Sandi Metz

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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.