Feedforward neural networks are a type of artificial neural network characterized by a unidirectional flow of information, with connections between nodes arranged in layers that do not form cycles. This architecture typically includes input, hidden, and output layers, utilizing activation functions such as sigmoid, tanh, and ReLU. Training is conducted through backpropagation, which minimizes loss and optimizes performance. Feedforward neural networks are widely used in applications such as image and speech recognition, providing significant advantages to researchers and developers in the fields of machine learning and artificial intelligence.
