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Backpropagation utilizes the chain rule of calculus to compute the gradient of the loss function with respect to each weight in the network. The chain rule allows the decomposition of the gradient into a series of simpler, local gradients that can be efficiently calculated layer by layer, from the output layer back to the input layer. Join our AI community for more posts like this @aibutsimple 🤖 #machinelearning #deeplearning #math #datascience
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AI • Machine Learning • Tech on Instagram: "Backpropagation, the algorithm that efficiently computes gradients for training neural networks, went through several key stages in AI history. Early neural network research in the 1960s, including work by Marvin Minsky and Seymour Papert, highlighted serious limitations of simple perceptrons, which contributed to reduced interest in neural networks for a time. In the 1970s, Paul Werbos proposed using the chain rule to propagate error gradients backwar
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