piilotason
Piilotason is the Finnish term for the hidden layer in artificial neural networks. It is the layer between the input layer and the output layer of a feedforward network. Each neuron in a piilotason receives a weighted sum of inputs from the previous layer, applies a nonlinear activation function, and passes its output to the next layer. The piilotason enables the network to learn representations of data that are not linearly separable, contributing to the model’s capacity to capture complex patterns.
Typically, a network includes one or more piilotason depending on the architecture. A shallow network has a
Challenges include vanishing or exploding gradients in deep networks; regularization methods like dropout or weight decay,
Applications span image and speech recognition, language processing, and structured data tasks. The concept originated with