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YouTube

Deep Dive into Large Language Models: From ChatGPT to Training and Applications

Andrej Karpathy via YouTube

Overview

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Embark on a comprehensive 3.5-hour video lecture by Andrej Karpathy, founding member of OpenAI and former Sr. Director of AI at Tesla, exploring the intricate world of Large Language Models (LLMs) like ChatGPT. Gain deep insights into the complete training stack of these models, from pretraining data and tokenization to neural network architectures and reinforcement learning. Learn about the 'psychology' of LLMs, their practical applications, and how to effectively utilize them in your work. The lecture covers crucial topics including model hallucinations, tool use, knowledge representation, supervised fine-tuning, and reinforcement learning from human feedback (RLHF). Through detailed examples and demonstrations using tools like GPT-2 and Llama 3.1, understand the evolution of LLM technology, current capabilities, and future developments. Access numerous resources, visualization tools, and practical implementations shared throughout the lecture to enhance your understanding of state-of-the-art AI systems.

Syllabus

00:00:00 introduction
00:01:00 pretraining data internet
00:07:47 tokenization
00:14:27 neural network I/O
00:20:11 neural network internals
00:26:01 inference
00:31:09 GPT-2: training and inference
00:42:52 Llama 3.1 base model inference
00:59:23 pretraining to post-training
01:01:06 post-training data conversations
01:20:32 hallucinations, tool use, knowledge/working memory
01:41:46 knowledge of self
01:46:56 models need tokens to think
02:01:11 tokenization revisited: models struggle with spelling
02:04:53 jagged intelligence
02:07:28 supervised finetuning to reinforcement learning
02:14:42 reinforcement learning
02:27:47 DeepSeek-R1
02:42:07 AlphaGo
02:48:26 reinforcement learning from human feedback RLHF
03:09:39 preview of things to come
03:15:15 keeping track of LLMs
03:18:34 where to find LLMs
03:21:46 grand summary

Taught by

Andrej Karpathy

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