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What is the sigmoid function, and what is its use in machine learning's neural networks?
What is the vanishing gradient problem?
The vanishing gradient problem is a phenomenon where the gradients of the loss function with respect to the parameters of the neural network become very small as they propagate back through many layers of the network, making it difficult to train the network effectively. This can occur with the sigmoid and tanh activation functions, but is less of a problem with ReLU.