What is the vanishing gradient problem in deep learning?
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Answer / Anubha Rastogi
The vanishing gradient problem is a challenge faced during training deep neural networks, particularly with vanilla backpropagation. It occurs when the gradients of the loss function become very small as we move deeper into the network layers. This makes it difficult for the network to learn complex representations and optimize its weights effectively. Various solutions have been proposed, such as rectified linear units (ReLUs), Leaky ReLUs, and other optimization algorithms.
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