dumitrue

7 ms·

31.
by dumitrue 14y ago
That's an interesting (and relevant) question. In fact, if you're using something like stochastic gradient descent to optimize the weights of your network, it might be very hard for the network to escape the general local minimum (or basin
32.
by dumitrue 14y ago
Here's a paper describing the main idea behind doing this: http://ronan.collobert.com/pub/matos/2011_nlp_jmlr.pdf In a nutshell, you learn a vector of real-valued parameters for each word in your vocabulary. To train a network on sequence
33.
by dumitrue 14y ago
To some extent it's true that "deep learning" is a buzzword, though it is less arbitrary that "data scientist" (which makes no sense whatsoever). In a nutshell, what people mean by "deep learning" is the collection of tips and tricks that m
34.
by dumitrue 14y ago
For things like the number of layers, the total number of options is relatively small -- usually people try between 1 and 6-7. For most of the other parameters you have to be smarter than that, especially since a lot of them are real-valued
35.
by dumitrue 14y ago
It's not obvious to me what it means to have "better than human-level performance" since most of the time the ground-truth itself is defined by humans :)
36.
by dumitrue 14y ago
http://deeplearning.net/software/theano/ is a good place to start. It's open source, in Python, has a few tutorials that lead you towards some rather state-of-the-art methods. There's no secret sauce for how to choose the number of layers
37.
by dumitrue 14y ago
So what if the Teslas are built in the US? How does that change anything? What if they were built in Canada, like a lot of GM cars are these days? Or Japan?
38.
by dumitrue 14y ago
I see at least 170 openings in Sunnyvale alone.