HackerTube
HomeLongsLikedHistoryWatch laterCategoriesPlaylistsSettings

Large Language Models from scratch

Graphics in 5 Minutes3 HN recommenders · recommended in 2 months, 2023-2024
YouTube
375K views · 4 years ago · explainer · evergreen

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.

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

LLMs & generative AIMachine learning & deep learning#llm#neuralnetworks#gradientdescent#transformers#languagemodeling

5 comments from Hacker News

@mistermann2024 · in “The Starship or the Canoe: Where will our future adaptations be? (2017)”

(Note: I am a different Human than the one you are replying to - apologies for interjecting.) > "Physically preventable" is a moving target. So are rockets and artillery shells, but check this out: link Humans can be very clever and generous ($60,000 USD per interceptor missile, hoo baby!! Is "democracy" not the greatest thing ever!!!???) when they set their minds to something. Unfortunately, delusion, untruthfulness, deceit, misdirection, etc are also somethings , and apparently very desirable somethings, seemingly irresistible to some. > The more resources are invested in preventing currently preventable deaths, the more farther away the target moves, because more and more causes of death become preventable. Something seems off here - when you say "farther away", what is the unit of measure? To make sense (if you can pardon the colloquialism) of this argument, I think it would help if you gave a quantitative example to prove that your fact is actually factual. My intuition is that you may be conflating [1] physical death (concrete) with causes of death (abstract). > So, this is a false argument. A problem for you - some of us know of this phenomenon: link ... and not just abstractly . There is a very interesting tangential conversation related to this seemingly [1] minor distinction - perhaps some day (some) Humans will have such conversations. I suspect this ("this 'is' [1] a false argument") may be another one of those not actually factual facts. link > There's no p

CitesView on HN
@runetech2023 · in “Ask HN: Can someone ELI5 Transformers and the “Attention is all we need” paper”

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 ;-) Large Language Models from scratch ▶ this video Large Language Models: Part 2 link

RecommendsView on HN
@peter_d_sherman2023 · in “Large Language Models from scratch (2022)”

Consider the following frame/time from the video: ▶ this 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 functions -- much like the Fourier Series and Taylor Series can... Now, I don't hold an advanced Ph.D. degree in Math, and prior to watching this video, I had never made the connection between Fourier Series, Taylor Series, and Neural Networks... I knew about each of those independently -- but prior to watching the video, I never made the connection that they can be used to approximate one another. And that's the one of the key words -- approximate . The other key word is Identity -- as in Mathematical Identity ... That's because IF two Mathematical Objects (functions, algorithms, call them what-you-will) are approximately equal -- then there should be a set of conditions (i.e., "If and only if...", etc.), a set of rigorously defined mathematical constraints which, if those conditions/constraints are met, then the two Mathematical Objects are exactly precisely equivalent -- under those rigorously defined conditions/constraints.... In other words, under those rigorously defined conditions/constraints -- one Mathematical Object could be substituted for another -- with no loss of precision or result. In other words, under those rigorously defined conditions/constraints -- those two completely apparently different Mathematical

Strongly recommendsView on HN
@sainez2023 · in “A Completely Non-Technical Explanation of Deep Learning”

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

Strongly recommendsView on HN
@mistermann2023 · in “Globalization Is Dead and No One Is Listening”

> What is so unserious, disingenuous, and illegitimate about the “system”? Some examples (I could write indefinitely): link (section "Bank Error In Our Favor" is particularly offensive, to me) link (this sort of thing (which is fairly common) is offensive to me, as is our culture's complacency towards it) How it is (in part) perpetuated, in my estimation: - I’m sure whatever the system is it isn’t perfect. But here I am using the “system” and living my life. [rationalization] - I see so many strange comments on the internet that sound like “red-pilled” nonsense. [training-based heuristic pattern matching to ~bad/dumb/wrong] - Could it be better and is there room for improvement? Absolutely. But living in parallel red pill reality is an absolutely unhelpful endeavor . [training-based heuristic pattern matching to ~bad/dumb/wrong + cultural normalization of hallucination of reality (in this case: the future)] - After-all, you use the real system to have a life and so does everyone else. Might as well face that fact and truly engage with flesh and blood in the meat space rather than whatever or whoever is on red pill virtual land. [Stay within the Overton Window of ideas and behaviors one has been trained on] > Where would you rather be and what is your improvement on the “system”? a) How about something like "The contents of the tin (upon substantial, fine-grained inspection) matches what's on the label"? b) we shall see, maybe > If you have a good personal value propositio

CitesView on HN
Transformers, the tech behind LLMs | Deep Learning Chapter 527:14

Transformers, the tech behind LLMs | Deep Learning Chapter 5

3Blue1Brown
11.6M views • 2 years ago
Deep Dive into LLMs like ChatGPT3:31:24

Deep Dive into LLMs like ChatGPT

Andrej Karpathy
10.2M views • 1 year ago
[1hr Talk] Intro to Large Language Models59:48

[1hr Talk] Intro to Large Language Models

Andrej Karpathy
4.3M views • 2 years ago
A Hackers' Guide to Language Models1:31:13

A Hackers' Guide to Language Models

Jeremy Howard
580K views • 3 years ago
GPT3: An Even Bigger Language Model - Computerphile25:57

GPT3: An Even Bigger Language Model - Computerphile

Computerphile
446K views • 6 years ago
Let's build GPT: from scratch, in code, spelled out.1:56:20

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

Andrej Karpathy
8.2M views • 3 years ago
AI Language Models & Transformers - Computerphile20:39

AI Language Models & Transformers - Computerphile

Computerphile
354K views • 7 years ago
The spelled-out intro to language modeling: building makemore1:57:45

The spelled-out intro to language modeling: building makemore

Andrej Karpathy
1.3M views • 4 years ago
LSTM is dead. Long Live Transformers!28:48

LSTM is dead. Long Live Transformers!

Seattle Applied Deep Learning
540K views • 6 years ago
State of GPT | BRK216HFS42:40

State of GPT | BRK216HFS

Microsoft Developer
785K views • 3 years ago
Let's build the GPT Tokenizer2:13:35

Let's build the GPT Tokenizer

Andrej Karpathy
1.3M views • 2 years ago
But how do AI images and videos actually work? | Guest video by Welch Labs37:20

But how do AI images and videos actually work? | Guest video by Welch Labs

3Blue1Brown
2.2M views • 1 year ago
An Observation on Generalization57:21

An Observation on Generalization

Simons Institute for the Theory of Computing
210K views • 3 years ago
Attention in transformers, step-by-step | Deep Learning Chapter 626:10

Attention in transformers, step-by-step | Deep Learning Chapter 6

3Blue1Brown
4.8M views • 2 years ago
OpenAI Codex Live Demo29:43

OpenAI Codex Live Demo

OpenAI
913K views • 5 years ago
What do translators think about GPT?34:20

What do translators think about GPT?

Tom Gally
2K views • 3 years ago
CS480/680 Lecture 19: Attention and Transformer Networks1:22:38

CS480/680 Lecture 19: Attention and Transformer Networks

Pascal Poupart
374K views • 7 years ago
Ch(e)at GPT? - Computerphile13:52

Ch(e)at GPT? - Computerphile

Computerphile
617K views • 3 years ago
Do we need Attention? - Linear RNNs and State Space Models (SSMs) for NLP39:43

Do we need Attention? - Linear RNNs and State Space Models (SSMs) for NLP

Sasha Rush
35K views • 3 years ago
Attention Is All You Need27:07

Attention Is All You Need

Yannic Kilcher
814K views • 8 years ago