Andrej Karpathy｜Let's build GPT: from scratch, in code 。。。

 

Andrej Karpathy 前特斯拉AI 負責人。

 

Jan 17, 2023

 

Chapters:

00:00:00 intro: ChatGPT, Transformers, nanoGPT, Shakespeare baseline language modeling, code setup

00:07:52 reading and exploring the data

00:09:28 tokenization, train/val split

00:14:27 data loader: batches of chunks of data

00:22:11 simplest baseline: bigram language model, loss, generation

00:34:53 training the bigram model

00:38:00 port our code to a script Building the "self-attention"

00:42:13 version 1: averaging past context with for loops, the weakest form of aggregation

00:47:11 the trick in self-attention: matrix multiply as weighted aggregation

00:51:54 version 2: using matrix multiply

00:54:42 version 3: adding softmax

00:58:26 minor code cleanup

01:00:18 positional encoding

01:02:00 THE CRUX OF THE VIDEO: version 4: self-attention

01:11:38 note 1: attention as communication

01:12:46 note 2: attention has no notion of space, operates over sets

01:13:40 note 3: there is no communication across batch dimension

01:14:14 note 4: encoder blocks vs. decoder blocks

01:15:39 note 5: attention vs. self-attention vs. cross-attention

01:16:56 note 6: "scaled" self-attention. why divide by sqrt(head_size) Building the Transformer 01:19:11 inserting a single self-attention block to our network

01:21:59 multi-headed self-attention

01:24:25 feedforward layers of transformer block

01:26:48 residual connections

01:32:51 layernorm (and its relationship to our previous batchnorm)

01:37:49 scaling up the model! creating a few variables. adding dropout Notes on Transformer

01:42:39 encoder vs. decoder vs. both (?) Transformers

01:46:22 super quick walkthrough of nanoGPT, batched multi-headed self-attention

01:48:53 back to ChatGPT, GPT-3, pretraining vs. finetuning, RLHF

01:54:32 conclusions

Corrections:

00:57:00 Oops "tokens from the future cannot communicate", not "past". Sorry! :)

 

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