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Where'd My Gradient Go? It Vanished!
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This video discusses the vanishing gradient problem, a significant challenge in training deep neural networks. The speaker explains how, as a neural network becomes deeper, gradients—measures of how changes in network parameters affect the loss function—can decrease exponentially, leading to a situation where early layers of the network are effectively frozen and unable to learn. This problem arises because common activation functions like the sigmoid function can produce very small derivatives, which compound during backpropagation. The video then explores solutions like using different activation functions (like ReLU) and architectural changes (like residual networks and LSTMs) to mitigate this issue.
Watch the video: https://www.youtube.com/watch?v=ncTHBi8a9uA&pp=ygUSdmFuaXNoaW5nIGdyYWRpZW50
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