> if you want to learn about deep learning, just learn about neural networks. It's the same thing.
Hi, could you explain this further? From my limited understanding, couldn't deep learning be achieved without neural networks? If we define deep learning as something like automatic feature extraction on one level of hierarchy in order to aid classification at a different level. I understand that you are saying that neural networks are the most common tool used, however what is the reason for this -- couldn't one also stack the results of other algorithms (decision trees, SVM, etc.) into a hierarchy of features?
Sure. In 100% of articles you've seen in the last few years re: "Deep learning", it's about neural networks. It's not that neural nets are magic, it's just that stacking them has been called "deep learning" and that's the name that seems to be sticking. I'll even be cynical and say neural nets got such a bad rep by the 90s that they needed a new name.
Re: stacking SVMs and decision trees for feature hierarchies: I guess you could (maybe), but you'd get worse results than if you used neural nets. And definitionally it wouldn't be deep learning.
Hi, could you explain this further? From my limited understanding, couldn't deep learning be achieved without neural networks? If we define deep learning as something like automatic feature extraction on one level of hierarchy in order to aid classification at a different level. I understand that you are saying that neural networks are the most common tool used, however what is the reason for this -- couldn't one also stack the results of other algorithms (decision trees, SVM, etc.) into a hierarchy of features?