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These days any method that uses gradient descent to optimize a computational graph gets branded as deep learning. It's a very general paradigm that allows for a
by m_ke 9y ago
These days any method that uses gradient descent to optimize a computational graph gets branded as deep learning. It's a very general paradigm that allows for almost any composition of functions as long as it's differentiable. If that's not composable then I don't know what is.
There's a reason why Lecun wanted to rebrand deep learning as differentiable programming. https://www.facebook.com/yann.lecun/posts/10155003011462143 https://www.facebook.com/yann.lecun/posts/10155003011462143
I'm not sure what wirrbel meant.
- taeric 9y agoMy guess for what they meant was that you can't compose the trained models. For example, a classifier that tells you cat or not can't be used with one that says running or not to get running cat. The benefit being that you could put together more "off the shelf" models into products. Instead, you have to train up pretty much everything from the ground. And we compare against others doing the same. @wirrbel, that accurate?