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Loss Functions in Deep Learning

📑 5 slides 👁 6 views 📅 2/27/2026
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Introduction to Loss Functions

Loss functions measure how well a model's predictions match actual data

Introduction to Loss Functions
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Common Regression Loss Functions

  • Mean Squared Error (MSE): Squares differences, sensitive to outliers
  • Mean Absolute Error (MAE): Robust to outliers, less sensitive than MSE
  • Huber Loss: Combines MSE and MAE benefits with delta threshold
  • Used for continuous value prediction like house prices or temperatures
Common Regression Loss Functions
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Classification Loss Functions

  • Cross-Entropy: Measures probability distribution differences, common for classification
  • Binary Cross-Entropy: Specialized for yes/no classification tasks
  • Categorical Cross-Entropy: For multi-class problems with one-hot encoding
  • KL Divergence: Measures difference between two probability distributions
Classification Loss Functions
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Specialized Loss Functions

  • Hinge Loss: Used in SVM and some neural networks for classification
  • Contrastive Loss: For siamese networks learning similarity metrics
  • Triplet Loss: Learns embeddings by comparing anchor, positive, negative samples
  • Custom loss functions can be designed for specific applications
Specialized Loss Functions
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Choosing & Optimizing Loss

  • Select based on problem type: regression, classification, ranking etc.
  • Consider mathematical properties like convexity and differentiability
  • Combine multiple losses for complex tasks (multi-task learning)
  • Proper loss choice significantly impacts model training and performance
Choosing & Optimizing Loss
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