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 single hidden layer; deep networks have multiple hidden layers. During training, the weights of the piilotason are adjusted by backpropagation using gradient-based optimization to minimize a loss function. Activation functions such as ReLU, sigmoid, or tanh are applied at the piilotason neurons to introduce nonlinearity.

Challenges include vanishing or exploding gradients in deep networks; regularization methods like dropout or weight decay, architectural innovations like residual connections, and normalization techniques help. The choice of the number of hidden units and layers, as well as the activation function, affects model capacity and generalization.

Applications span image and speech recognition, language processing, and structured data tasks. The concept originated with early multilayer perceptrons and backpropagation in the 1980s and became central to modern deep learning in the 2010s with larger datasets and computational advances.