In machine learning, backpropagation is a gradient computation method commonly used for training a neural network in computing parameter updates. It is an efficient application of the chain rule to neural networks.
Backpropagation, short for Backward Propagation of Errors, is a key algorithm used to train neural networks by minimizing the difference between predicted and actual outputs.
Backpropagation is a machine learning technique essential to the optimization of artificial neural networks. It facilitates the use of gradient descent algorithms to update network weights, which is how the deep learning models driving modern artificial intelligence (AI) “learn.”
This is the whole trick of backpropagation: rather than computing each layer’s gradients independently, observe that they share many of the same terms, so we might as well calculate each shared term once and reuse them. This strategy, in general, is called dynamic programming.
Here we tackle backpropagation, the core algorithm behind how neural networks learn. If you followed the last two lessons or if you’re jumping in with the appropriate background, you know what a neural network is and how it feeds forward information.
In this chapter I'll explain a fast algorithm for computing such gradients, an algorithm known as backpropagation. The backpropagation algorithm was originally introduced in the 1970s, but its importance wasn't fully appreciated until a famous 1986 paper by David Rumelhart, Geoffrey Hinton, and Ronald Williams.
Backpropagation, short for "backward propagation of errors," is an algorithm for supervised learning of artificial neural networks using gradient descent. Given an artificial neural network and an error function, the method calculates the gradient of the error function with respect to the neural network's weights.
Neural networks are like brain-inspired math machines they learn by trial and error. But how do they know what to fix when they get something wrong? The answer is backpropagation — the math magic...
Introduced in the 1970s, the backpropagation algorithm is the method for fine-tuning the weights of a neural network with respect to the error rate obtained in the previous iteration or epoch, and this is a standard method of training artificial neural networks.
