Deep Residual Network
As per the attached image, a deep residual network, or a deep ResNet, is a type of specialized neural network that helps to handle more sophisticated deep learning tasks and models. A deep residual network has received quite a bit of attention at recent IT conventions and is being considered for helping with the training of deep networks. A deep residual network example shows that ResNet solves degradation problems by shortcuts or skip connections by short-circuiting shallow layers to deep layers. A deep residual network is designed by using EdrawMax, and in the best-case scenario, additional layers of the deep neural network can better approximate the mapping of x to output y than it’s the shallower counterpart and reduces the error by a significant margin.
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