Backpropagation
The algorithm for calculating gradients of the loss function with respect to network weights.
In-depth explanation
Backpropagation efficiently computes how each weight contributes to the overall error by propagating error signals backward through the network using the chain rule of calculus. Combined with gradient descent, it enables training of deep neural networks. The algorithm made modern deep learning possible and remains fundamental to neural network training.
Examples
Related terms
More in Neural Networks
Neural Network
A computing system inspired by biological neural networks, consisting of interconnected nodes (neurons).
Neuron
A basic computational unit in a neural network that receives inputs, applies weights and activation, and produces output.
Activation Function
A mathematical function that determines the output of a neuron based on its weighted input sum.
Epoch
One complete pass through the entire training dataset during model training.
Batch Size
The number of training examples used in one iteration of model training.
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