A short Coursera course from DeepLearning.AI and AWS covering how large language models are built, trained, and adapted, including scaling, fine-tuning, and reinforcement learning from human feedback. It is an overview of the current state of generative AI rather than a deep technical treatment.

Commenters call it a great foundational course on LLMs and a decent survey for newcomers, though one notes it is partly sales material for AWS.

On Reddit (5)

1. Strongly recommendsihaagr/LocalLLaMA2023in “Behind the Curtain: How do we look inside a Llama model file to browse the data?”

   I suggest taking this awesome course: https://www.coursera.org/learn/generative-ai-with-llms

2. Strongly recommendslevel1gamerr/LocalLLaMA2023in “How do I learn about LLM's?”

   This course was a great foundational course on LLMs: https://www.coursera.org/learn/generative-ai-with-llms

3. RecommendsAndreeSmothersr/learnmachinelearning2023in “resources to learn about training LLMs?”

   llmops.space has great educational materials that you can check out. This Coursera lesson is also perfect for beginners https://www.coursera.org/learn/generative-ai-with-llms

4. Recommendsboddilur/learnmachinelearning2023in “A roadmap to understand the theory behind LLMs”

   I'd suggest auditing courses on coursera.org / deeplearning.ai The "deep learning" specialization on coursera sets out a roadmap of 5 courses, but if you want to go straight to LLMs, you can probably get by just watching the first Neural Networks and Deep Learning to get a basic foundation on the…

5. MentionsConsistent_Area9877r/MachineLearning2023in “[D] How large an LLM can I train from scratch on a single A100 GPU with 80Gb memory?”

   Take a look at this course https://www.coursera.org/learn/generative-ai-with-llms The scaling section where they mentioned the numbers. 1.5B is just my estimate because the instructor said you can train 1B param using 16-bit precision and that’ll be 40 GB estimate.

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