Deep Learning / 05
Loss Functions
Compare loss functions for regression, classification, representation learning, and other objectives.
This is a knowledge index. Published titles open their articles; the remaining entries outline planned coverage.
Fundamentals
- 05.01
What is a Loss Function?
Planned guide: What is a Loss Function. Focus on intuition, assumptions, and a worked example.
- 05.02
Loss vs Metric
Planned guide: Loss vs Metric. Focus on intuition, assumptions, and a worked example.
- 05.03
Objective Function
Planned guide: Objective Function. Focus on intuition, assumptions, and a worked example.
Regression
- 05.04
Mean Squared Error
Planned guide: Mean Squared Error. Focus on intuition, assumptions, and a worked example.
- 05.05
Mean Absolute Error
Planned guide: Mean Absolute Error. Focus on intuition, assumptions, and a worked example.
- 05.06
Huber Loss
Planned guide: Huber Loss. Focus on intuition, assumptions, and a worked example.
Classification
- 05.07
Binary Cross-Entropy
Planned guide: Binary Cross-Entropy. Focus on intuition, assumptions, and a worked example.
- 05.08
Categorical Cross-Entropy
Planned guide: Categorical Cross-Entropy. Focus on intuition, assumptions, and a worked example.
- 05.09
Sparse Categorical Cross-Entropy
Planned guide: Sparse Categorical Cross-Entropy. Focus on intuition, assumptions, and a worked example.
- 05.10
Negative Log-Likelihood
Planned guide: Negative Log-Likelihood. Focus on intuition, assumptions, and a worked example.
Advanced
- 05.11
Contrastive Loss
Planned guide: Contrastive Loss. Focus on intuition, assumptions, and a worked example.
- 05.12
Triplet Loss
Planned guide: Triplet Loss. Focus on intuition, assumptions, and a worked example.
- 05.13
Hinge Loss
Planned guide: Hinge Loss. Focus on intuition, assumptions, and a worked example.
- 05.14
Focal Loss
Planned guide: Focal Loss. Focus on intuition, assumptions, and a worked example.
- 05.15
KL Divergence
Planned guide: KL Divergence. Focus on intuition, assumptions, and a worked example.
- 05.16
Perceptual Loss
Planned guide: Perceptual Loss. Focus on intuition, assumptions, and a worked example.
- 05.17
Custom Loss Functions
Planned guide: Custom Loss Functions. Focus on intuition, assumptions, and a worked example.