I really disagree. > It excels when you have massive amounts of data to begin with, but fails catastrophically otherwise. Then what is zero shot learning and

I really disagree. > It excels when you have massive amounts of data to begin with, but fails catastrophically otherwise. Then what is zero shot learning and does this not represent a major advance? Sure, you need lots of data, but then it learns things not even in the distribution. That's pretty cool. > And encoding domain specific knowledge about your data is the most important trick of creating good models and loss functions This is true of all machine learning in general, but I don't think it's more true of DL specifically. > so deep learning won't "automatically solve your problem" unless you're doing something cookie-cutter like image classification. Remember a few years ago when image classification was hard? Let's take a moment to appreciate how many things deep learning made easier in the last decade. Now it automatically solves it, with more and more models pretrained for tasks released on huggingfaces every day. > tuck it in their toolbelt as one of their various tools, and move on. Disagree, if I'm training any classifier, I'm starting that work in pytorch (after data wrangling of course). Deep learning changed the entire field. Learn it. Use it.