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The Forward Pass of a Feedforward Neural Network

A feedforward network transforms an input by alternating affine maps and nonlinear activations: each layer computes a weighted sum z = Wx + b from the previous layer's activations and applies an activation function a = f(z). Learners trace an input through the layers by hand, identify the weights, biases, pre-activations, and activations at each layer, and state which quantities are stored during the forward pass.

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Forward Propagation in Neural Networks in Deep Learning

Forward propagation is the mechanistic process within deep learning where information traverses a neural network architecture sequentially from input through hidden layers to the output layer. This m…