bulldog13

Can anyone recommend a great, concise resource to learn LLM technical fundamentals including transformers, pre-training, fine-tuning, etc from the ground up starting with neural networks?

The resources I can find are either how to use langchain to write LLM apps, which is not what I want, or very deep graduate level book study, which I don't have the time for.

Something that starts with neural networks (which I am familiar with) and takes you all the way to how LLMs work, but as concise as possible.

My goal is to be able to talk about LLM technical fundamentals cogently, not necessarily code one from scratch.

Thanks!

Where do I begin studying if my goal is to run LLMs locally or in a private cloud with solid software engineering practices? I am unsure of where to even begin.

Let's say I want to:

0.) Find the locally run LLMs and identify which are applicable. 1.) Containerize the LLMs 2.) Use source control to capture changes to the LLM. Versioning output. 3.) Develop repeatable pipelines driven by APIs for sending data to it. 4.) Prompt engineering. 5.) Best ways to use langchain (or others) to make the system data-aware and agentic.

Any thoughts are appreciated.