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What is Feedforward vs Backward Propagation Explained about?

This document provides an in-depth overview of forward and backward propagation in feedforward neural networks. It introduces notations to represent nodes, layers, weights, biases, activations and other entities. Forward propagation is explained as computing predictions and cost based on these notations. Backward propagation uses the chain rule to compute gradients of the cost with respect to weights and biases in order to update them during training. Vectorization of equations is also discussed to take adv

Author

Vikash Kumar

Language

EN