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Adding bias nodes to a neural network is a common practice to improve its expressiveness and flexibility. A bias node is a special type of input, typically set to a constant value (often 1), which allows neurons to produce non-zero outputs even when all other inputs are zero.
Determining the number of hidden layers and nodes in a neural network is a critical step in model design, as it directly impacts the network's ability to learn patterns, its computational efficiency, and its ability to generalize to new data