S24 Recitation 0I: Datasets Part 1

Carnegie Mellon University Deep Learning30.1K subscribers

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Full-course playlist '11785 Spring 2024 Recitations' from a curated course list (11-785: Introduction to Deep Learning - CMU (Lectures - YouTube-2024, Recitations - YouTube-2024)); uploader is official; playlist judged: Official CMU channel's recitation series for a real graduate deep learning course, covering substantive technical and practical ML content.; title passed a strict title check; oEmbed verified.

What you'll learn

A 7-minute video from Carnegie Mellon University Deep Learning: “S24 Recitation 0I: Datasets Part 1”.

Programming & CS

Video by Carnegie Mellon University Deep Learning, played through YouTube's embedded player.

1:23:04

IDL Spring 2024: Lecture 3 Carnegie Mellon University Deep LearningProgramming & CS

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The Beauty of Bézier Curves Freya HolmérProgramming & CS

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CS50x 2024 - Lecture 1 - C CS50Programming & CS

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CS50x 2024 - Lecture 0 - Scratch CS50Programming & CS

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

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1:23:04

IDL Spring 2024: Lecture 3

Carnegie Mellon University Deep Learning

A 83-minute video from Carnegie Mellon University Deep Learning: “IDL Spring 2024: Lecture 3”.

1:00:48

S24 Recitation 5

Carnegie Mellon University Deep Learning

A 61-minute video from Carnegie Mellon University Deep Learning: “S24 Recitation 5”.

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

3Blue1Brown

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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 Karpathy

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

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3Blue1Brown

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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

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

24:26

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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.

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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.

2:04:55

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.