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Feedforward Neural Network

A feedforward neural network passes data in one direction from the input layer through one or more hidden layers to an output. Each layer applies learned weights, biases, and activation functions to the preceding values. Unlike recurrent networks, the basic architecture has no loop that carries a hidden state through time. Multilayer perceptrons are a common feedforward form and can approximate complex nonlinear functions when given sufficient capacity. They are widely used for structured data and as components inside larger systems, including transformer blocks. Performance depends on architecture, initialization, normalization, regularization, data quality, and optimization rather than simply adding more layers or neurons.

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