Vanishing Gradients: Why Deep Networks Couldn t Learn (and the One-Line Fix) | Sam Schlesinger
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Vanishing Gradients: Why Deep Networks Couldn t Learn (and the One-Line Fix) | Sam Schlesinger
Deep learning was able to get deep because of a discovery in 2015, the residual layer. Before this, the gradient signal would decay to zero when trying to train the earliest layers of a feed-forward network. Concretely, this meant that we were stuck with nets less than 50 layers. After He et al. published their ResNet paper in 2015, the ...
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