Forward Propagation

Forward propagation is the computation a neural network performs from its input layer toward its output. At each layer, incoming values are multiplied by learned weights, combined with biases, and transformed by activation functions before moving onward. During training, the resulting prediction is compared with the correct target to calculate a loss; backpropagation then uses that loss to determine how weights should change. During inference, the forward pass produces the model’s answer without that training update. Its speed and memory requirements depend on architecture, input size, numerical precision, batching, and hardware, making forward-pass optimization central to low-latency and large-scale AI services.