Cross-Entropy Loss

Definitions

1. A loss function that measures how far predicted probability distributions are from the target distribution. 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: Cross-Entropy Loss 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 Cross-Entropy Loss 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
• Active Learning
• Adaptive Learning Rate
• Adversarial Training
• AutoML
• Backpropagation
• Batch Size
• Contrastive Loss
• K-Nearest Neighbors