Both Sigmoid and Tanh activation functions can cause the vanishing gradient problem. This issue occurs because these functions squash their inputs into a very small range, leading to very small gradients during backpropagation, which slows down learning. In deep neural networks, this can prevent the weights from updating effectively, causing the training process to stall.
Sigmoid: Outputs values between 0 and 1. For large positive or negative inputs, the gradient becomes very small.
Tanh: Outputs values between -1 and 1. While it has a broader range than Sigmoid, it still suffers from vanishing gradients for larger input values.
ReLU, on the other hand, does not suffer from the vanishing gradient problem since it outputs the input directly if positive, allowing gradients to pass through. However, Softplus is also less prone to this problem compared to Sigmoid and Tanh.
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Reference:
Deep Learning Overview: Explains the vanishing gradient problem in deep networks, especially when using Sigmoid and Tanh activation functions.
AI Development Framework: Covers the use of ReLU to address the vanishing gradient issue and its prevalence in modern neural networks.