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Technically deep learning started before 2008. Here is a trends paper from back then: http://www.cs.toronto.edu/~fritz/absps/tics.pdf http://www.cs.toronto.ed

by daveguy 10y ago

Technically deep learning started before 2008. Here is a trends paper from back then: http://www.cs.toronto.edu/~fritz/absps/tics.pdf http://www.cs.toronto.edu/~fritz/absps/tics.pdf Here is a google tech talk from 2007: https://www.youtube.com/watch?v=AyzOUbkUf3M https://www.youtube.com/watch?v=AyzOUbkUf3M Companies didn't pick it up until more recently. GPU-ification happened in 2009 with Ng's group: http://robotics.stanford.edu/~ang/papers/icml09-LargeScaleUnsupervisedDeepLearningGPU.pdf http://robotics.stanford.edu/~ang/papers/icml09-LargeScaleUn... And yes, Krizhevsky et al (Hinton's lab) applied GPU deep learning to ImageNet in 2010: https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf https://papers.nips.cc/paper/4824-imagenet-classification-wi...

strebler 10y ago
Those are great links! But the 2008 Hinton paper would not be considered deep learning, it is classic neural nets. It makes no mention of CNNs or GPUs, which is what really got this all going back in 2012 with ImageNet / Krizhevsky. The ImageNet paper is from 2012, not 2010. That's when the computer vision community really went "wow". IIRC, almost every entry in ImageNet 2013 was using CNNs.
- mattkrause 10y ago
  > it is classic neural nets. It makes no mention of CNNs or GPUs Is using a GPU "essential" for something to be deep learning? I'd always thought that the important part was some sort of hierarchical representation learning. GPUs certainly help, in that you don't want to wait all day while your code does that, but they're not necessary.
  - edfernandez 10y ago
    I think Tsvi Achler's video here will be useful to understand better what the article is about https://www.youtube.com/watch?v=9gTJorBeLi8 https://www.youtube.com/watch?v=9gTJorBeLi8
- daveguy 10y ago
  Good call on the 2012 not 2010 date. I missed that. GPU are not requirements of deep nn. Hinton's pseudo-bayesian + ReLU approach was the last piece of the deep neural net functionality. CNNs dated back to 1995-1998 with LeCun and Bengio. Although GPUs do accelerate deep NNs enough to be feasible on image data (thanks to Ng).

by daveguy 10y ago

strebler 10y ago

Those are great links! But the 2008 Hinton paper would not be considered deep learning, it is classic neural nets. It makes no mention of CNNs or GPUs, which is what really got this all going back in 2012 with ImageNet / Krizhevsky. The ImageNet paper is from 2012, not 2010. That's when the computer vision community really went "wow". IIRC, almost every entry in ImageNet 2013 was using CNNs.

mattkrause 10y ago
> it is classic neural nets. It makes no mention of CNNs or GPUs Is using a GPU "essential" for something to be deep learning? I'd always thought that the important part was some sort of hierarchical representation learning. GPUs certainly help, in that you don't want to wait all day while your code does that, but they're not necessary.
- edfernandez 10y ago
  I think Tsvi Achler's video here will be useful to understand better what the article is about https://www.youtube.com/watch?v=9gTJorBeLi8 https://www.youtube.com/watch?v=9gTJorBeLi8
daveguy 10y ago
Good call on the 2012 not 2010 date. I missed that. GPU are not requirements of deep nn. Hinton's pseudo-bayesian + ReLU approach was the last piece of the deep neural net functionality. CNNs dated back to 1995-1998 with LeCun and Bengio. Although GPUs do accelerate deep NNs enough to be feasible on image data (thanks to Ng).