feedforwardketens

A feedforward network, also known as a feedforward neural network or multilayer perceptron, is a type of artificial neural network where connections between nodes do not form a cycle. Information moves in only one direction, forward, from the input nodes, through any hidden layers, and finally to the output nodes. There are no loops or cycles in the network.

These networks are characterized by layers of neurons. The input layer receives the raw data. One or more hidden layers process this data, performing complex transformations. Finally, the output layer produces the result of the computation. Each connection between neurons has an associated weight, and during the learning process, these weights are adjusted to minimize the error between the network's predictions and the actual target values.

Feedforward networks are fundamental to many machine learning tasks. They are commonly used for classification, regression, and pattern recognition. Their simplicity and effectiveness have made them a foundational concept in deep learning, serving as the building blocks for more complex architectures. Training typically involves algorithms like backpropagation, which efficiently computes the gradient of the loss function with respect to the weights.