Which activation function is recommended for use in hidden layers of a neural network?

The ReLU (Rectified Linear Unit) activation function is widely recommended for use in the hidden layers of a neural network due to its advantages in training deep networks. ReLU is defined as ( f(x) = \max(0, x) ), which means it outputs the input directly if it is positive; otherwise, it outputs zero. This property helps in mitigating the vanishing gradient problem, which can occur with other activation functions, especially sigmoid or tanh, as networks become deeper.

The non-linear nature of ReLU allows networks to learn complex patterns in data while maintaining computational efficiency, as it involves simple thresholding at zero. Additionally, ReLU activates neurons sparsely, which contributes to a more efficient representation within the network. This activation function is effective in allowing faster convergence during training, thus making it a popular choice among practitioners in the field of artificial intelligence.

In contrast, the Heaviside function is discontinuous and not ideal for gradient-based optimization, while the Softmax function is specifically used for output layers in multi-class classification tasks. A linear activation function does not introduce non-linearity, which is critical for enabling the network to learn complex relationships in data, making it unsuitable for hidden layers in most scenarios.