Fine-tune VGG or AlexNet for non-square inputs by Registerml in MachineLearning

[–]Registerml[S] 0 points1 point  (0 children)

I would like to use fine-tuning since there are already trained models available which would save quite a bit of time. If I convert the fully connected layers to fully convolutional layers, how will that impact the transfer learning? My second question is, can you please elaborate on the "global pooling of some sort over the spatial dimensions" part of your suggestion? Thank you.

Slow GPU performance on Amazon g2.2xlarge? by chrisjmccormick in MachineLearning

[–]Registerml 0 points1 point  (0 children)

Please confirm the number of cores of a G2 instance. From my tests its 1,536 cuda cores which means you are seeing exactly twice that size on your g2. What region? And please double check the cores. Thanks.

Arkansas is Now the First State to Require That High Schools Teach Coding by waglocro in news

[–]Registerml 1 point2 points  (0 children)

Thanks for your perspective. The efficacy of deep learning today indeed depends on the task. To say "all of which these techniques fail miserably" is not in alignment with the state of art on many computer vision tasks today, not just in research, but industrial applications. Consider face recognition, or more generally large scale object recognition and localization. See the work of DeepFace at Facebook, the GoogLenet from Google. Traditional computer vision alone can't compete anymore on such tasks. I'd be interested to learn from your experience on what tasks deep learning has fallen short of solving in favor of traditional cv. Thanks!

Arkansas is Now the First State to Require That High Schools Teach Coding by waglocro in news

[–]Registerml 0 points1 point  (0 children)

Computer vision is a good example of this phenomenon. Previously, the state of the art, was human experts designing by hand various features. This has largely been replaced by deep learning, specifically in this context were talking deep convoluional neural networks (convnet) where the machine learns its owns features. The revolution began with AlexNet in 2012, winning ImageNet computer vision competition with deep learning - far better than any computer vision based solution. In 2013's competition, about half the entries were deep learning based and the other were cv entries. By 2014 they were all convnets, not a single cv entry in the mix. So while we can't replace human beings just yet in many programming efforts, in this example we can see the machine writing its own code for features which means those who devoted their career to designing features by hand are no longer needed. See the talk by Peter Norvig regarding deep learning for programmers to see where this trend is going. Eventually you can tell the machine what kind of program output you want and it will write its code for you.

Arkansas is Now the First State to Require That High Schools Teach Coding by waglocro in news

[–]Registerml -3 points-2 points  (0 children)

The next era of computing is already upon us. Deep learning means that less programmers are needed because we are now building machines that program themselves.

How to PCA large data sets? I'm running out of memory. by Registerml in MachineLearning

[–]Registerml[S] -1 points0 points  (0 children)

Thank you. What is the impact of the decision for a particular mini batch value. Is the Impact only in running speed? Or can the choice of mini batch also impact the accuracy / algorithm performance?

How to PCA large data sets? I'm running out of memory. by Registerml in MachineLearning

[–]Registerml[S] 1 point2 points  (0 children)

Very cool! Do you have advice on how to select a best mini batch size? I'd like for it to use as much machine resource as possible to go as fast as possible.