Earlier quoted context omitted.
I agree. Thanks for the link. I have added stuff that i started with. will improve this list over time.
Added the link to the list..
Awesome!
Deep Learning Reading List
11–16 of 16 posts
Re: Deep Learning Reading List
#12Don't forget "Intriguing properties of neural networks", otherwise known as "Does Deep Learning have deep flaws?".
In sum, you can teach a network to say "that's a dog" when presented with a picture of a dog, but you'll also be able to (1) find an imperceptibly modified version of the image where it'll say "that's garbage" and (2) intelligently generate an image of noise that also gets recognised as a dog.
(1) http://arxiv.org/abs/1312.6199 (2) http://www.newscientist.com/article/dn26691-optical-illusion...
Re: Deep Learning Reading List
#13Don't forget "Intriguing properties of neural networks", otherwise known as "Does Deep Learning have deep flaws?". In sum, you can teach a network to say "that's a dog" when presented with a picture of a dog, but you'll also be able to (1) find an imperceptibly modified version of the image where it'll say "that's garbage" and (2) intelligently generate an image of noise that also gets recognised as a dog. (1) http:/…
In (2)'s paper [2], the images are really neat. Staring at it for a few moments, I feel like I can see where the computer is coming from. A bit.
The robin, armadillo, centipede, peacock, and bubble all actually have a little swirl of features that - to me at least - resemble the labels provided. But from afar, and you've got to basically damp the noise. I did this by taking my glasses off and leaning about 8 inches from the screen (with the grid taking about 3 inches wide). I've got about -7.5 diopter near-sightedness, so this cleaned the images right up. At least the armadillo I would have guessed, as it's absolutely a little critter walking to the lower right, and it has the demi-circle body and long face. And it might make sense: it was told to make an armadillo, so it did. And nothing else.
(I also think the baseball was super clever - if this were abstract art, I would totally have fallen for that classification, as well as a few of the others. There's something cool going on there.)
I'm printing and reading the rest of the paper now (about a quarter done). To say it's both fascinating and interesting really understates it. Really neat stuff!
Re: Deep Learning Reading List
#14Karpathy's JavaScript demos are the first thing that I would show to a newcomer:
http://cs.stanford.edu/people/karpathy/convnetjs/
Re: Deep Learning Reading List
#15Don't forget "Intriguing properties of neural networks", otherwise known as "Does Deep Learning have deep flaws?". In sum, you can teach a network to say "that's a dog" when presented with a picture of a dog, but you'll also be able to (1) find an imperceptibly modified version of the image where it'll say "that's garbage" and (2) intelligently generate an image of noise that also gets recognised as a dog. (1) http:/…
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Re: Deep Learning Reading List
#16Deep learning videos and lectures are good. Great list.