Effective Strategies for Debugging and Optimizing Machine Learning Models
Explore different scenarios of model performance including overfitting, underfitting, and optimization issues in machine learning. Learn how to identify and address these challenges to improve model accuracy and efficiency during training.
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Presentation Transcript
The (ideal) plots Train Val Accuracy Loss Train Val Iterations during training Iterations during training
Overfitting (mild) Validation accuracy does not improve as training loss goes down Train Val Accuracy Loss Train Val Iterations during training Iterations during training
Overfitting (bad) Validation accuracy decreases as training loss goes down Train Val Accuracy Loss Train Val Iterations during training Iterations during training
Underfitting Train Val Accuracy Loss Train Val Iterations during training Iterations during training
Underfitting vs overfitting Train Val Loss Overfitting Underfitting Model capacity
Optimization issues: Large step size Train Small step sizes cause slow learning, Large step sizes cause divergence Loss Iterations during training
SGD with momentum Stochastic gradient is stochastic Can reduce variance using momentum Standard update: With momentum:
Optimization issues: convergence Train Val Accuracy Loss Train Val Iterations during training Iterations during training
