Model Training  &  Optimization

Part 1

1.Training Process Understanding

Master the model training process and defining training objectives.

2.Loss Functions and Metrics

Comprehend the significance of loss functions and evaluation metrics.

3.Optimization Technique

Experiment with various optimization algorithms and learning rate schedules.

4.Overfitting Prevention

Implement regularization techniques to prevent overfitting.

5.Transfer Learning Benefits

Leverage pre-trained models through transfer learning for improved performance.

6. Hyperparameter Tuning

Optimize model performance through thorough hyperparameter tuning.

7.Handling Imbalanced Datasets

Implement techniques for handling imbalanced datasets.

8.Model Weight Initialization

Experiment with different weight initialization methods.

9. Impact of Batch Size & Epochs

Understand how batch size and training epochs affect model convergence.

10.Interpretability Techniques

Implement data augmentation techniques to enhance model training with limited data.