Backpropagation
Noun · AI & Machine Learning
Definitions
The algorithm that computes gradients through a network so weights can be updated efficiently. It influences how models are trained, evaluated, or served, and it can materially change accuracy, robustness, latency, cost, or interpretability. Practitioners usually track it alongside data quality, compute limits, and validation results when moving models into production.
In plain English: Backpropagation is an AI concept that affects how a model learns, predicts, or gets deployed. It matters because it changes quality, speed, or reliability.
Example: "We revisited Backpropagation during model evaluation because the first run looked fine offline but behaved poorly in production, and the adjustment improved quality without breaking our latency or compute budget."
Related Terms
- Quantization
- Activation Function
- Active Learning
- Adaptive Learning Rate
- Adversarial Training
- Autoencoder
- AutoML
- Bayesian Neural Network
- Bayesian Optimization
- Boltzmann Machine
- Cross-Entropy Loss
- Federated Averaging
- Flow Matching
- GAN
- Gated Recurrent Unit
- Graph Convolutional Network
- Graph Neural Network
- Knowledge Distillation
- Sigmoid Function
- Neural Network Pruning