TeodolfoM

Support vector machines are not particularly interesting. They are just perceptrons with a lot of math. So ultimately souped-up linear classifiers. If you start learning the kernel in an SVM you get a deeper model. A neural net using the hinge loss and an L2 penalty on the weights is basically a primal SVM.

A link to a textbook draft from prominent ML researchers specializing in deep learning that is shockingly also about deep learning is not an example of hype. Yoshua Bengio has been working on neural nets for years and years and he leads the only one of the big three deep learning labs to not have a lot of commercial entanglements. I agree there seems to be some extra attention for deep learning recently. So who is hyping this stuff? Not the people in the big three (Toronto, NYU, Montreal) deep learning labs. They are just doing what they have always done, work on neural nets and solve problems and write papers (and in this case a textbook).

Google, Microsoft, Facebook, Baidu, and to a lesser extent IBM, have all made large commitments to using deep learning methods. Why? Because they solve some really hard problems particularly well. Are they hyping deep learning? Not as such. They are trying to bring attention to their products. Rick Rashid had a presentation that mentioned deep learning and some of the other firms have mentioned that they use deep learning in certain products (e.g. google voice search) but they don't really seem to be hyping the methods or techniques so much as their own particular successes with them. So who is hyping deep learning?

Deep learning techniques solve a lot of important problems and can almost certainly be applied to many more. But "deep learning" isn't a single technique, it is an attitude and approach to machine learning. So the reason you are hearing more about it is because big industry players are using it and some people in academia are getting a bit more traction in the broader ML community with the ideas they have been pushing, refining, and improving for years.

Shitty tech journalists and overoptimistic amateur "data science" clowns in the startup community that think these ideas are some sort of panacea are the people overhyping it.