An interview guide on common Machine Learning concepts, best practices, definitions, and theory.
Contents
- Model Scoring Metrics
- Parameter Sharing
- k-Fold Cross Validation
- Python Data Types
- Improving Model Performance
- Computer Vision Models
- Attention and its Variants
- Handling Class Imbalance
- Computer Vision Glossary
- Vanilla Backpropagation
- Regularization
- References
Model Scoring Metrics
- Classification Accuracy
- Log Loss
- Confusion Matrix
- Precision