Love the suggestions, especially the one about implementing papers.
Do you have any starters on how one selects papers in the early days, to implement?
Also - any great papers you recommend beginners expose themselves to?
Do you have any starters on how one selects papers in the early days, to implement?
Also - any great papers you recommend beginners expose themselves to?
I wouldn’t recommend papers to absolute beginners though. For them, it’s best to go to HuggingFace, find a model that seems interesting and play with it in a Jupyter notebook. You’ll get a lot more bang for your buck.
Those papers were within reach for me. Even if the math (or the collossal search effort) needed to find them was out of reach, implementing them wasn't.
There were some things besides optimizers and activation functions too. In particular I remember Dmitri Ulyanov's "Deep Image Priors" paper. He did publish code, but the thing he explored - using the implicit structure in a model architecture without training (or, training on just your input data!) is actually dead simple to try yourself.
I'm sure if you just drink from the firehose of the arxiv AI/ML feeds, you'll find something that tickles your interest that you can actually implement. Or at least play with published code.