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Large Language Models from scratch

A short cartoon explaining how language models like ChatGPT work, from autocomplete and conditional probabilities to neural networks, gradient descent and backpropagation. It shows how networks act as universal function approximators.

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Why HN readers recommended it

HN recommends it as a quick, non-dumbed-down way to get up to speed on how LLMs are built.

This is a summary from the source dataset, not an endorsement by Hacker News or the video creator.

Video & recommendation details

Creator / channel
Graphics in 5 Minutes
Topic
large language models
Format
explainer
Level
intro
HN recommenders in dataset
3
HN mentions in dataset
5
Recommendation years in dataset
2023–2024

These counts and years describe the included Hacker News records; they are not YouTube views or the video’s publication date.

From the original HN conversations

Excerpts from the recorded comments. Follow each source for the full comment and surrounding discussion.

  • If anyone is looking for a quick overview of how LLMs are built, I highly recommend this video by Steve Seitz: [VIDEO] . It does an excellent job of taking you from 0 to a decent understanding without dumbing down the content or abusing analogies.

    sainez on Hacker News · 2023-04
    In A Completely Non-Technical Explanation of Deep Learning

  • Consider the following frame/time from the video: [VIDEO] At this point in time in the video, we see the following 3 things: 1) Fourier Series 2) Taylor Series 3) Neural Network The remainder of the video goes on to show how Neural Networks can approximate …

    peter_d_sherman on Hacker News · 2023-05
    In Large Language Models from scratch (2022)

  • Not sure I can give an ELI5 explanation, but for me this the closest to what you are after in two short videos on the topic. At least they helped me get up to speed fast. Hope they will do the same for you ;-) …

    runetech on Hacker News · 2023-05
    In Ask HN: Can someone ELI5 Transformers and the “Attention is all we need” paper

Summaries, classifications and recommendation records are from the HackerTube source dataset. Read about this library and its limitations.

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