As a software engineer, I disagree. Caveat, I haven't studied traditional ML and just went straight to DL. There is a lot of jargon and you do have to sit down and learn how things work, but once you do, deep learning is fairly simple. One thing that actually really bothers me is how much libraries (e.g. huggingface) are just config files masquerading as programming. It is just a class with 50 parameters and takes about 5 lines of code to make it do the thing, most of the time is spent figuring out what the parameters do.
I think making lower level tools (fundamental building blocks) will make things more accessible to software engineers, as opposed to the high level wrappers being written. We can grab a library, read the docs, and put pieces together in an efficient way. We just need some core work horse libraries. Like if llama.cpp was a library at the same maturity as sqlite.
I think making lower level tools (fundamental building blocks) will make things more accessible to software engineers, as opposed to the high level wrappers being written. We can grab a library, read the docs, and put pieces together in an efficient way. We just need some core work horse libraries. Like if llama.cpp was a library at the same maturity as sqlite.