On 02/05/2014 06:40 PM, Kyle Kastner wrote: > Not to bandwagon extra things on this particular effort, but one > future consideration is that if scikit-learn supported multilayer > neural networks, and eventually multilayer convolutional neural > networks, it would become feasible to load pretrained nets ALA > OverFeat, DeCAF (recent papers with sweet results) and use them as > transforms. > > I am doubtful about the ability to train a reasonably deep neural > network without GPU, specialized hardware, or a server, but I think > loading pretrained coefficients and using them as a transform is very > reasonable. It may be too "messy" for adoption in scikit-learn, but an > adapter layer could be very useful - I know this is basically what I > and other competitors used for a recent kaggle competition with great > success. > > That was convolutional nets for images, right? Or did you have some other pretrained net? I don't think convnets for images are in the scope, and an adaptor should rather live in a project like DeCAF.
------------------------------------------------------------------------------ Managing the Performance of Cloud-Based Applications Take advantage of what the Cloud has to offer - Avoid Common Pitfalls. Read the Whitepaper. http://pubads.g.doubleclick.net/gampad/clk?id=121051231&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
