Feed-Forward Neural Network Architecture Design
Design feed-forward neural network architectures by selecting appropriate layer types, activation functions, and output configurations for different problem domains.
Feed-forward networks (also called fully connected networks or multilayer perceptrons) pass data through stacked layers of neurons in one direction: input → hidden layers → output. Architecture design involves choosing: number of hidden layers (depth), neurons per layer (width), activation functions (ReLU, tanh, sigmoid), and output layer structure (single neuron for regression, softmax for multi-class classification, sigmoid for binary). Each choice has consequences: deeper networks capture complex non-linear patterns but risk overfitting; ReLU activations enable deeper networks but can cause dead neurons; wider layers increase capacity but computational cost. Principled architecture design balances model expressiveness against regularization needs and computational…
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