PickVidsBest LLMs & generative AI Videos
The 108 LLMs & generative AI videos here were recommended by different people across different months. Choose by who it is for: 11 introductory, 29 intermediate, 65 for a general viewer, and 3 for someone who wants the harder derivation. Most of them are a demo. What they share is that you see the result first and only then decide whether to follow the detail. Channels you will keep seeing include Lex Fridman, Andrej Karpathy, Computerphile.
Transformers, the tech behind LLMs | Deep Learning Chapter 5
3Blue1Brown's visual explanation of how transformers and GPT-style language models work, tracing data through tokens, embeddings and attention.
Deep Dive into LLMs like ChatGPT
Andrej Karpathy's three-hour-plus general-audience deep dive into how ChatGPT-style LLMs are built, covering the full training stack from pretraining to post-training.
Let's build GPT: from scratch, in code, spelled out.
Andrej Karpathy builds a GPT from scratch in code, following Attention Is All You Need and GPT-2/3, in about two hours using PyTorch.
A Hackers' Guide to Language Models
Jeremy Howard's 90-minute practical tour of language models.
[1hr Talk] Intro to Large Language Models
Andrej Karpathy gives a one-hour general-audience introduction to large language models, covering what they are, how they are trained, comparisons to operating systems, and security challenges such as jailbreaks.
Sparks of AGI: early experiments with GPT-4
Sebastien Bubeck presents the Sparks of AGI paper at MIT, showing early experiments with GPT-4 across many domains and asking whether artificial intelligence has arrived.
Let's build the GPT Tokenizer
Andrej Karpathy builds the GPT tokenizer from scratch, implementing byte pair encoding along with encode and decode.
Attention in transformers, step-by-step | Deep Learning Chapter 6
3Blue1Brown's visual walkthrough of the attention mechanism inside transformers, covering self-attention, multiple heads and cross-attention.
The A.I. Dilemma - March 9, 2023
Tristan Harris and Aza Raskin of the Center for Humane Technology present why current AI capabilities pose catastrophic societal risks.
LSTM is dead. Long Live Transformers!
Leo Dirac explains how transformer models have replaced LSTMs in NLP.
Attention Is All You Need
Yannic Kilcher walks through the Attention Is All You Need paper, which introduced the Transformer, a sequence model based solely on attention without recurrence or convolutions.
Playing a Neural Network's version of GTA V: GAN Theft Auto
Sentdex demonstrates GAN Theft Auto, a GameGAN-based neural network that recreates GTA V so the network itself is the playable environment.
ANIME ROCK, PAPER, SCISSORS
Corridor Digital makes a roughly seven-minute anime short using Stable Diffusion and related AI tools, based on a technique they teach in a longer tutorial.
Will AI outsmart human intelligence? - with 'Godfather of AI' Geoffrey Hinton
Geoffrey Hinton's 2025 Royal Institution Discourse on what AI has learned from biological intelligence and how it might surpass it.
Building pi in a World of Slop — Mario Zechner
Mario Zechner explains why he built pi, a minimal coding agent, out of frustration with existing agent harnesses.
State of GPT | BRK216HFS
Andrej Karpathy walks through how GPT assistants are trained, from tokenization and pretraining to supervised finetuning and RLHF, and gives practical prompting strategies and a view of the tooling ecosystem.
Do we need Attention? - Linear RNNs and State Space Models (SSMs) for NLP
Sasha Rush surveys linear RNNs and state space models as replacements for attention in transformer-style models, in a talk for MLSys.
GPT-4chan: This is the worst AI ever
Yannic Kilcher explains how he trained a language model on three years of 4chan's /pol/ board and let it post on the site.
CS480/680 Lecture 19: Attention and Transformer Networks
A university lecture from CS480/680 by Pascal Poupart on attention mechanisms and transformer networks, worked through on a chalkboard.
The spelled-out intro to language modeling: building makemore
Andrej Karpathy builds a bigram character-level language model from scratch, introducing torch.Tensor and the framework of training, sampling and loss evaluation.
I tried using AI. It scared me.
Tom Scott describes trying ChatGPT to fix his Gmail labels and being unsettled by how capable it was.
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.
Chris Lattner: Future of Programming and AI | Lex Fridman Podcast #381
Lex Fridman's long podcast conversation with Chris Lattner, creator of Swift, LLVM and MLIR and co-founder of Modular.
AI Language Models & Transformers - Computerphile
Rob Miles explains on Computerphile how language models generate plausible text and how transformers improved on recurrent networks, with a look at what comes next.