<ul data-eligibleForWebStory="true"><li>The degradation problem occurs in neural networks as they get deeper, causing performance to deteriorate due to challenges like vanishing gradients and overfitting.</li><li>Deeper networks are harder to train but are important for achieving leading results, like those on the ImageNet dataset.</li><li>A solution to deeper models involves adding identity mapping layers copied from shallower models to prevent higher training errors.</li><li>Learning identity mapping is difficult in neural networks with many nonlinear layers due to the challenges of preserving data perfectly.</li><li>The degradation problem is addressed by introducing a deep residual learning framework in neural networks.</li><li>ResNet introduces a residual connection where F(x) = H(x) - x, allowing the network to naturally learn the residual component to reach the desired output.</li><li>PyTorch implementation of the residual block includes self.block(x) as the residual function and adds the original input back to get the final output.</li><li>The loss function is computed based on the final output, optimizing the residual function F(x) to improve network performance.</li></ul>

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