backpropagation

Backpropagation is an algorithm used to improve the accuracy of predictions in neural networks, which are systems designed to mimic the way the human brain processes information. When a neural network is being trained, it makes predictions and then checks to see how far off these predictions are from what is actually true. Backpropagation helps the network to learn from its mistakes by going backwards through the network, from the output to the input, adjusting the weights of the connections between the nodes (neurons) based on the error of the prediction. This adjustment is done to reduce the error in future predictions and is typically repeated many times over the network to refine its accuracy[1][2][3][4][5][6].

Citations:

[1] https://en.wikipedia.org/wiki/Backpropagation

[2] https://youtube.com/watch?v=S5AGN9XfPK4

[3] https://towardsdatascience.com/understanding-backpropagation-algorithm-7bb3aa2f95fd

[4] https://www.techopedia.com/definition/17833/backpropagation

[5] https://towardsdatascience.com/laymans-introduction-to-backpropagation-efa2c64437db

[6] https://brilliant.org/wiki/backpropagation/

[7] https://neptune.ai/blog/backpropagation-algorithm-in-neural-networks-guide

[8] https://deepai.org/machine-learning-glossary-and-terms/backpropagation