ResNets — Residual Blocks & Deep Residual Learning

 By Medium - 2020-12-02

Description

Deep Learning harnesses the power of Big Data by building deep neural architectures that try to approximate a function f(x) that can map an input, x to its corresponding label, y. The Universal…

Summary

  • ResNets tackle the issue of performance degradation associated with the deep neural networks as they go deeper into the network.
  • If the activations for the layer l+2 tends to 0, Figure 5 This identity mapping created by these residual blocks is the reason why the addition of extra layers does not affect a residual network’s performance.
  • The layer can also make use of different filter sizes, including 1×1, padding, and strides to control the dimension of the output volume.
  • Summary A residual network is formed by stacking several residual blocks together.

 

Topics

  1. Machine_Learning (0.59)
  2. Backend (0.13)
  3. NLP (0.06)

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