Archives for Backpropagation in Neural Networks
Hinton’s experiments found that the FF algorithm had a 1.4 percent test error rate on the MNIST dataset which is just as effective as backpropagation and the two are comparable on the CIFAR-10 dataset
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The backpropagation algorithm computes the gradient of the loss function with respect to the weights. these algorithms are complex and visualizing backpropagation algorithms can help us in understanding its procedure in neural network.