Backpropagation
Backpropagation is the primary algorithm used to train neural networks. It works by calculating the gradient of the loss function with respect to the weights of the network, and then propagating that error backward through the layers using the chain rule to update parameters.
Frequently Asked Questions
Who popularized backpropagation?▼
Geoffrey Hinton and colleagues in 1986 popularized the algorithm for training multi-layer networks.
What optimization algorithms are paired with backpropagation?▼
Gradient Descent, Stochastic Gradient Descent (SGD), and Adam optimizer are commonly used to adjust weights based on gradients.
Quick Facts
- CategoryMathematical Foundations
- Key ApplicationNeural network training, optimization, and parameter adjustment
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