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Java Deep Learning Projects by Md. Rezaul Karim

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Residual neural networks

Because of the millions of billions of hyperparameters and other practical aspects associated with them, it is difficult to train deep neural networks. To overcome this limitation, Kaiming He et al. (see https://arxiv.org/abs/1512.03385v1) proposed a residual learning framework (RNN) to ease the training of networks that are substantially deeper than those used previously. Now, according to the original paper:

"In this network setting, instead of hoping each stack of layers directly fits a desired underlying mapping, we explicitly let these layers fit a residual mapping. The original mapping is recast into F(x)+x. We hypothesize that it is easier to optimize the residual mapping than to optimize the original, unreferenced ...

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