MLSP Fall 2024 | Lecture 1 - Introduction

Machine Learning for Signal Processing CMU402 subscribers

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Full-course playlist 'Fall 2024' from a curated course list (11-755 - Machine Learning for Signal Processing, CMU (YouTube-2024, YouTube-2023)); uploader is instructor; playlist judged: Full-length university lecture series on machine learning for signal processing, uploaded by the course's own channel, covering substantive technical topics in depth.; title passed a strict title check; oEmbed verified.

What you'll learn

A 85-minute video from Machine Learning for Signal Processing CMU: “MLSP Fall 2024 | Lecture 1 - Introduction”.

Programming & CS

Video by Machine Learning for Signal Processing CMU, played through YouTube's embedded player.

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The Beauty of Bézier Curves Freya HolmérProgramming & CS

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46:44

F23 Lecture 20 Factor Analysis

Machine Learning for Signal Processing CMU

A 47-minute video from Machine Learning for Signal Processing CMU: “F23 Lecture 20 Factor Analysis”.

1:19:56

F23 Lecture 17 SVM

Machine Learning for Signal Processing CMU

A 80-minute video from Machine Learning for Signal Processing CMU: “F23 Lecture 17 SVM”.

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

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

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

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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.

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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.

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