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This is essentially repeating your quote, but an "aha moment" clicked for me when I read that what successful neural networks are basically doing is such good f
by tvladeck 8y ago
This is essentially repeating your quote, but an "aha moment" clicked for me when I read that what successful neural networks are basically doing is such good feature learning that the problem can be solved by a simple linear model in the end.
E.g. if you have an N-layer neural network, N-1 layers are doing feature learning, and the Nth layer is a simple {logistic, multinomial/softmax, gaussian, poisson, ...} model
- halflings 8y agoRelated:"the kernel trick" [1]. "The kernel trick avoids the explicit mapping that is needed to get linear learning algorithms to learn a nonlinear function or decision boundary." (what powers Support Vector Machines, the neural networks of the 90s, and still alive and kicking today) [1] https://en.wikipedia.org/wiki/Kernel_method#Mathematics:_the_kernel_trick https://en.wikipedia.org/wiki/Kernel_method#Mathematics:_the...