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Deep Learning is a subset of machine learning that has revolutionized how we approach complex problems in various fields. This article delves into two fundamental concepts of deep learning: forward propagation and back propagation. Forward propagation is the initial phase of data processing in a neural network, where input data is fed into the network and passed through various layers. Back propagation is the learning phase, where the network compares the output to the desired outcome and adjusts the network’s weights and biases to minimize the error. Both methods come with challenges, but are integral to the functioning of a neural network.
