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A feed-forward neural network with one hidden layer models complex input–output relationships by passing information in a single direction—from inputs, through weighted connections, to outputs. Each input node represents a feature, such as a measurement, pixel value, or encoded word. These inputs are multiplied by trainable weights, summed with a bias term, and sent to neurons in the hidden layer. The hidden layer applies a nonlinear activation function (commonly ReLU, sigmoid, or tanh), which enables the network to capture ________ ___ ___ ____ ________ _____ ________ __________.
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