AI Termcirca 1974· Added Jun 1, 2026
Backpropagation
A supervised learning algorithm used to train artificial neural networks by adjusting weights through error propagation.
Backpropagation, short for 'backward propagation of errors,' is an essential mechanism for training neural networks. It works by computing the gradient of the loss function with respect to each weight through chain rule, automatically updating weights to reduce prediction errors iteratively. This process involves two phases: forward pass (computing output) and backward pass (adjusting weights using gradient descent). Backpropagation is highly efficient and enables deep learning models to learn complex patterns from data.
Examples
- Training convolutional neural networks (CNNs) in image recognition tasks using backpropagation.
- Fine-tuning recurrent neural networks (RNNs) for language modeling via backpropagation.
Common misconceptions
- It's a standalone algorithm. It's actually dependent on other algorithms like gradient descent for optimization.
- All layers learn equally well with backpropagation. Vanishing gradients can hinder early layers in deep networks.
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