# What is Backpropagation? Definition & Technical Mechanics

> **Source:** [SPIDITS AI AI Glossary](https://spidits.com/ai-glossary)  
> **Category:** Mathematical Foundations  
> **Canonical Citation:** [https://spidits.com/ai-glossary/backpropagation](https://spidits.com/ai-glossary/backpropagation)

## Definition
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.

## Architectural Mechanics & Deep Dive
Backpropagation (backward propagation of errors) is the fundamental algorithm used to train artificial neural networks. Working in tandem with gradient descent, it calculates the gradient of the loss function with respect to each weight in the network by applying the mathematical chain rule. The algorithm propagates error signals backward from the output layer to the input layer, allowing the optimizer to adjust weights to minimize overall prediction errors.

## Industry Applications & Real-World Use Cases
Neural network training, optimization, and parameter adjustment

## 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.

## Related Terminology
- [neural-network](https://spidits.com/ai-glossary/neural-network)
- [deep-learning](https://spidits.com/ai-glossary/deep-learning)
- [loss-function](https://spidits.com/ai-glossary/loss-function)

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*From the SPIDITS AI Knowledge Base (336+ terms indexed). Explore full technical definitions: [https://spidits.com/ai-glossary](https://spidits.com/ai-glossary)*