From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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70 lines
2.0 KiB
70 lines
2.0 KiB
WEBVTT |
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Friends. |
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I am absolutely exhausted. |
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I am exhausted and a little tiny bit traumatized. |
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And you are somewhat to blame for this, as you will discover. |
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You will discover in a moment you will find out why. |
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But first, let's talk about what we have in store for today. |
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So we are going to be raising the bar on our baselines. |
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I'm just going to take a moment to remind you what you can already do. |
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Of course, generating text and generating code from combinations of frontier models with AI assistants, |
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with tools, and also open source systems using hugging face Transformers library, you can use Lang |
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chain to build a rag pipeline, and now you can also curate data and you can curate it. |
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Finally, if I may say so. |
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And you can also make a baseline model using some rather foolish techniques. |
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But then using traditional machine learning linear regression, including both feature engineering and |
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bag of words, and then onto more sophisticated techniques using word two vec and then adding in support |
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vector machines and then random forests. |
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Quite a trek it's been. |
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So today. |
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Today we are going to now take the framework we put together and put it against frontier models. |
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And this will sort of really capture all of the steps it takes to take a proper business problem, um, |
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understand the data and then present it to frontier models and compare their performance. |
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So it's an exciting moment for us, and we will get right to it. |
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And I will see you over at JupyterLab in a moment.
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