Troubleshooting Fine-Tuning Challenges

• 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

This advanced-level course equips participants with the knowledge and skills to troubleshoot common challenges in fine-tuning machine learning models. From addressing data imbalances to resolving overfitting and ensuring proper model convergence, participants will gain practical expertise to handle real-world issues in fine-tuning scenarios.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to refine their skills in diagnosing and solving fine-tuning challenges for machine learning models.

By the end of this training, participants will be able to:

• Diagnose issues like overfitting, underfitting, and data imbalance.
• Implement strategies to improve model convergence.
• Optimize fine-tuning pipelines for better performance.
• Debug training processes using practical tools and techniques.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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