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 Duration 14 hours

Course Outline

Navigating Fine-Tuning Difficulties

  • An overview of the fine-tuning workflow
  • Typical obstacles when fine-tuning large-scale models
  • The effect of data quality and preprocessing on outcomes

Handling Data Imbalances

  • Recognizing and evaluating dataset imbalances
  • Methods for managing imbalanced data
  • Incorporating data augmentation and synthetic data

Controlling Overfitting and Underfitting

  • Distinguishing between overfitting and underfitting
  • Applying regularization methods: L1, L2, and dropout
  • Tuning model complexity and training duration

Enhancing Model Convergence

  • Troubleshooting convergence issues
  • Selecting appropriate learning rates and optimizers
  • Applying learning rate schedules and warm-up phases

Troubleshooting Fine-Tuning Workflows

  • Tools for tracking the training process
  • Recording and visualizing key model metrics
  • Identifying and fixing runtime errors

Maximizing Training Efficiency

  • Strategies for batch size and gradient accumulation
  • Leveraging mixed-precision training
  • Implementing distributed training for large models

Practical Troubleshooting Examples

  • Example: Fine-tuning for sentiment analysis
  • Example: Fixing convergence problems in image classification
  • Example: Mitigating overfitting in text summarization

Key Takeaways and Future Directions

Requirements

  • Proficiency with deep learning frameworks such as PyTorch or TensorFlow
  • A solid grasp of machine learning fundamentals, including training, validation, and evaluation
  • Experience in fine-tuning pre-trained models

Target Audience

  • Data scientists
  • AI engineers

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