CS 480/680 - F24 - L15 - Recurrent Neural Networks

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Full-course playlist 'CS480/680 Introduction to Machine Learning Fall 2024' from a curated course list (CS 480/680 Introduction to Machine Learning, Kathryn Simone, University of Waterloo (Fall 2024)); uploader is instructor; playlist judged: Full university machine learning course lecture series uploaded by the instructor with substantive, honestly-titled lectures covering standard ML curriculum.; title passed a strict title check; oEmbed verified.

What you'll learn

A 97-minute video from Kathryn Simone: “CS 480/680 - F24 - L15 - Recurrent Neural Networks”.

Programming & CS

Video by Kathryn Simone, played through YouTube's embedded player.

44:25

CS 480/680 - F24 - L9b - Gaussian Processes Kathryn SimoneProgramming & CS

1:23:43

CS 480/680 - F24 - L14 - Convolutional Neural Networks Kathryn SimoneProgramming & CS

But what is a neural network? | Deep learning chapter 1 3Blue1BrownScience & Math25.1M views

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

Transformers, the tech behind LLMs | Deep Learning Chapter 5 3Blue1BrownProgramming & CS11.6M views

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

24:26

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

2:27:41

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

2:04:55

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

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

44:25

CS 480/680 - F24 - L9b - Gaussian Processes

Kathryn Simone

A 44-minute video from Kathryn Simone: “CS 480/680 - F24 - L9b - Gaussian Processes”.

1:23:43

CS 480/680 - F24 - L14 - Convolutional Neural Networks

Kathryn Simone

A 84-minute video from Kathryn Simone: “CS 480/680 - F24 - L14 - Convolutional Neural Networks”.

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.

Let's build GPT: from scratch, in code, spelled out.

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

3Blue1Brown11.6M views

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

CS501.41M views

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

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

Ben Eater5.85M views

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

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

Confreaks65.7K 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.

2:04:55

CS50x 2024 - Lecture 0 - Scratch

CS502.2M views

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

RailsConf 2014 - All the Little Things by Sandi Metz

Confreaks190K 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.