PickVidsBest LLMs & generative AI Videos
「LLMs & generative AI」下面有108部被不同的人、在不同月份推荐过的视频。挑的时候先看对象：入门11部，适合还没学过的人；进阶29部，适合已经懂术语的人；普通向65部，按兴趣就能看；更硬的3部，留给想看推导或细节的人。这一类数量最多的是演示，所以它们的共同点是先看到做成了什么，再决定要不要追细节。常出现的频道包括Lex Fridman、Andrej Karpathy、Computerphile。目录给这个主题划的范围是：Large language models and generative AI: GPT, ChatGPT, transformers, attention, diffusion, image generation, prompting, AI coding assistants, deepfakes, synthetic media, AI products and demos.如果只看三部，就从《Transformers, the tech behind LLMs | Deep Learning Chapter 5》开始，它负责把问题讲出画面；然后看《Deep Dive into LLMs like ChatGPT》，换一种讲法或例子；最后看《Let's build GPT: from scratch, in code, spelled out.》，留给已经有基础的人。页面按每页24部切开，顺序看的是被推荐跨越了多少个月，不是播放量。每条标题下面那一句是这部片子自己的内容说明，用来区分它在讲定义、过程、演示还是观点。点进去能看到推荐原话、原文链接和适合谁看。看的时候把强烈推荐当成有人明确叫你去看，把引用当成只是拿来举例，把批评留下来对照，不要把每条评论都当成同一力度。
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.