Doing the impossible: Why neural networks can be trained at all

13 May 2018 Nathan O. Hodas Panos Stinis

As deep neural networks grow in size, from thousands to millions to billions of weights, the performance of those networks becomes limited by our ability to accurately train them. A common naive question arises: if we have a system with billions of degrees of freedom, don't we also need billions of samples to train it?.. (read more)

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