Loss Function
A Loss Function is a mathematical algorithm that measures the discrepancy between a model's predicted output and the actual true target value during training. The goal of training is to minimize this loss, adjusting weights based on gradients computed from it.
Frequently Asked Questions
What is Cross-Entropy Loss?▼
A loss function commonly used in classification tasks to measure the difference between two probability distributions (predicted probabilities vs. true labels).
What is Mean Squared Error (MSE)?▼
A loss function used in regression tasks that averages the squared differences between predicted and actual values.
Quick Facts
- CategoryMathematical Foundations
- Key ApplicationOptimization algorithms, cost metric calculation, and error assessment during training
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