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Course Outline
Introduction to Fine-Tuning Challenges
- Overview of the fine-tuning process
- Common challenges in fine-tuning large models
- Understanding the impact of data quality and preprocessing
Addressing Data Imbalances
- Identifying and analyzing data imbalances
- Techniques for handling imbalanced datasets
- Using data augmentation and synthetic data
Managing Overfitting and Underfitting
- Understanding overfitting and underfitting
- Regularization techniques: L1, L2, and dropout
- Adjusting model complexity and training duration
Improving Model Convergence
- Diagnosing convergence problems
- Choosing the right learning rate and optimizer
- Implementing learning rate schedules and warm-ups
Debugging Fine-Tuning Pipelines
- Tools for monitoring training processes
- Logging and visualizing model metrics
- Debugging and resolving runtime errors
Optimizing Training Efficiency
- Batch size and gradient accumulation strategies
- Utilizing mixed precision training
- Distributed training for large-scale models
Real-World Troubleshooting Case Studies
- Case study: Fine-tuning for sentiment analysis
- Case study: Resolving convergence issues in image classification
- Case study: Addressing overfitting in text summarization
Summary and Next Steps
Requirements
- Experience with deep learning frameworks like PyTorch or TensorFlow
- Understanding of machine learning concepts such as training, validation, and evaluation
- Familiarity with fine-tuning pre-trained models
Audience
- Data scientists
- AI engineers
14 Hours
