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A neural network organizes computation into layers. The input layer receives features measured from each example, such as pixel brightness values, exam-study hours, or word-count features; it usually does not transform them. Hidden layers combine incoming values using learned weights and biases, then apply activation functions so the model can represent non-linear patterns. The output layer converts the final hidden representation into a prediction. For example, a classifier may produce one score per class, while a regression model may produce one numerical estimate. The number of input and output units is determined by the problem, whereas hidden-layer width is a design choice.

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Deep networks can solve complex tasks because successive layers learn representations at increasing levels of abstraction. A vision model, for instance, can combine local edges into textures, parts, and full objects; a language model can combine tokens into phrases and broader meaning. This hierarchical reuse often represents complicated functions more efficiently than a shallow network with a similar number of units. Depth is not automatically beneficial, however. Deep models can require large labeled datasets, substantial computing power, and careful tuning. They may overfit, inherit bias from data, be difficult to interpret, or suffer from vanishing and exploding gradients. Regularization, normalization, good data practices, and validation help manage these limitations.