Vanishing Gradient Problem
The vanishing gradient problem is a common issue in deep neural networks where the gradients of the loss function become extremely small as they propagate backwards through the network, making it difficult for the model to learn and update the weights of the earlier layers. This occurs when the activation function used in the network has a derivative that is close to zero, causing the gradient to shrink exponentially as it moves backwards through the layers. As a result, the earlier layers of the network receive very little information about the error and are not updated effectively, leading to poor performance. The vanishing gradient problem can be mitigated by using activation functions with larger derivatives, such as ReLU, or by using techniques such as batch normalization and residual connections.
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