4 ms·
My perspective is that, ML/DL is just another tool in the shed, with its own advantages and disadvantages. It excels when you have massive amounts of data to be
by cyber_kinetist 5y ago
My perspective is that, ML/DL is just another tool in the shed, with its own advantages and disadvantages. It excels when you have massive amounts of data to begin with, but fails catastrophically otherwise. And encoding domain specific knowledge about your data is the most important trick of creating good models and loss functions, so deep learning won't "automatically solve your problem" unless you're doing something cookie-cutter like image classification.
The verdict is that people in every scientific/engineering field should learn a bit of ML/DL (the underlying theory in it is not that hard), think a bit about the benefits and costs of it, tuck it in their toolbelt as one of their various tools, and move on. There's no harm in learning about the new trendy stuff, but keep in mind that there's no point in banging even the most high-quality hammer at a screw.
- dave_sullivan 5y agoI 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.