In standard Recurrent Neural Networks (RNNs), the Tanh activation function is commonly used in the hidden layers. The Tanh function squashes input values to a range between -1 and 1, allowing the network to learn complex patterns over time by transforming the input data into non-linear patterns.
While other activation functions like Sigmoid can be used, Tanh is preferred in many RNNs for its wider range. ReLU is generally used in feed-forward networks, and Softmax is often applied in the output layer for classification problems.
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Reference:
Deep Learning Overview: Describes the architecture of RNNs, highlighting the use of Tanh as the standard activation function.
AI Development Framework: Discusses the various activation functions used across different neural network architectures.