Comment by cdavid
12 hours ago
Indeed. I would add a third factor to compute and datasets: the lego-like aspect of NN that enabled scalable OSS DL frameworks.
I did some ML in mid 2000s, and it was a PITA to reuse other people code (when available at all). You had some well known libraries for SVM, for HMM you had to use HTK that had a weird license, and otherwise looking at experiments required you to reimplement stuff yourself.
Late 2000s had a lot of practical innovation that democratized ML: theano and then tf/keras/pytorch for DL, scikit learn for ML, etc. That ended up being important because you need a lot of tricks to make this work on top of "textbook" implementation. E.g. if you implement EM algo for GMM, you need to do it in the log space to avoid underflow, DL as well (gorot and co initialization, etc.).
Remember watching Alec Radford's Theano tutorial and feeling like I had found literal gold.
I think your post may have more acronyms than any other post I have ever read on hn. Do you have a guide to which specific things you are talking about with each acronym? Deep Learning and Machine Learning are obvious but some of the others I can’t follow at all - they could be so many different things.
NN - neural networks OSS DL frameworks - open source deep learning frameworks
PITA - pain in the ass
SVM - support vector machines HMM - hidden Markov model EM - expectation maximization GMM - gaussian mixture model HTK - hidden Markov model tool kit
I think he maintains pinball machines and jukeboxes for a chain of Greek restaurants