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Course Outline

Introduction to Fine-Tuning Challenges

  • An overview of the fine-tuning workflow
  • Common difficulties encountered when fine-tuning large models
  • The impact of data quality and preprocessing on outcomes

Mitigating Data Imbalances

  • Identifying and analyzing data distribution skew
  • Techniques for managing imbalanced datasets
  • Leveraging data augmentation and synthetic data

Handling Overfitting and Underfitting

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

Enhancing Model Convergence

  • Diagnosing convergence-related issues
  • Selecting the optimal learning rate and optimizer
  • Incorporating learning rate schedules and warm-up phases

Troubleshooting Fine-Tuning Pipelines

  • Utilizing tools to monitor training progress
  • Logging and visualizing key model metrics
  • Identifying and fixing runtime errors

Boosting Training Efficiency

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

Practical Troubleshooting Case Studies

  • Case study: Fine-tuning models for sentiment analysis
  • Case study: Resolving convergence problems in image classification
  • Case study: Mitigating overfitting in text summarization tasks

Summary and Recommended Next Steps

Requirements

  • Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow
  • A solid grasp of core machine learning concepts, including training, validation, and evaluation
  • Prior familiarity with the process of fine-tuning pre-trained models

Target Audience

  • Data scientists
  • AI engineers
 14 Hours

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