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Thank you, I was afraid that was the case, but having the options clearly arrayed before me makes it far less daunting!
I suspect that my usual method of using GPT-4 will run into issues with a lot of newer techniques and functions falling outside the September 2021 knowledge cutoff. It's a fast moving field after all.
Hmm, basically all the libraries I listed except maybe for pytorch haven't changed all that much since 2021, gpt-4 should really still be very useful with all of them. What it will have trouble with is a library like "Transformer" by huggingface, which lets you automatically download and use pretrained deep learning models. But to even use a super-high-abstraction library like that one you still need a bunch of "glue skills" like knowing how to load a .png image from your computer into a format that the high-level functions can understand, and how to interpret and visualise the output of those high-level functions. GPT-4 would be amazing for all of that.
I think Transformers has been around. Langchain on the other hand...
That said, you can use search enabled GPT or bing for this stuff too.
But- I have noticed GPT will mess up when setting up ML models sometimes. Stuff like mixing up the ordering of dimensions. I wound up with a toy transformers model that was trying to learn a string reversal task using positional embeddings that were spread across batches instead of across tokens the other day. And on other tasks it has all sorts of other minor errors. The code usually compiles- but it doesn't always do what you thought you asked for.
It certainly gives you experience doing critical thinking with regards to code debugging though, while also doing a lot to help you learn new libraries. The loop goes from
Read Demos and example code and textbooks -> Write hopefully passable code -> Debug code
to
Have GPT4 write weird, buggy or technically correct but silly code -> Debug Code while asking GPT4/googling/inferring/testing what the functions do.
Which I much prefer.
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