If the backpropagation algorithm estimates that increasing a given neuron’s activity will improve the output prediction, for example, then that neuron’s weights will increase. The goal is to change ...

The algorithm, called backpropagation, was the spark that fired up the current revolution of deep learning as the de facto machine learning behemoth. At its core, “backprop” is an extremely effective ...

This is part 2 of this tutorial, and in this is we will look at Backpropagation for entire Convolutional Neural Network. In part 1, we already saw the backpropagation for convolutional operation.

Training a neural network is the process of finding values for the weights and biases so that, for a set of training data with known input and output values, the computed outputs of the network ...

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Backpropagation is not limited to function derivatives. Any algorithm that effectively takes the loss function and applies gradual, positive changes back through the network is valid.

Back Propagation is a common method of training artificial neural networks so as to minimize objective function. This paper describes the implementation of back propagation algorithm.