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Duration 14 hours
Course Outline
Introduction to the Fine-Tuning Process
- Defining the concept of fine-tuning
- Exploring use cases and the advantages of fine-tuning
- An overview of pre-trained models and transfer learning concepts
Preparation for Fine-Tuning
- Strategies for collecting and cleaning datasets
- Identifying task-specific data requirements
- Conducting exploratory data analysis and preprocessing
Core Fine-Tuning Techniques
- Applying transfer learning and feature extraction methods
- Utilizing Hugging Face to fine-tune transformers
- Distinguishing between fine-tuning for supervised and unsupervised tasks
Refining Large Language Models (LLMs)
- Adapting LLMs for specific NLP tasks such as text classification and summarization
- Training LLMs using custom proprietary datasets
- Controlling LLM outputs and behavior through prompt engineering
Performance Optimization and Evaluation
- Techniques for hyperparameter tuning
- Methods for assessing model performance
- Mitigating issues related to overfitting and underfitting
Scaling Fine-Tuning Operations
- Implementing fine-tuning on distributed systems
- Utilizing cloud-based solutions to enhance scalability
- Reviewing case studies of large-scale fine-tuning projects
Best Practices and Managing Challenges
- Adopting best practices for successful fine-tuning outcomes
- Addressing common pitfalls and troubleshooting techniques
- Considering ethical implications in the fine-tuning of AI models
Advanced Topics (Optional)
- Fine-tuning multi-modal model architectures
- Understanding zero-shot and few-shot learning paradigms
- Exploring Low-Rank Adaptation (LoRA) techniques
Conclusion and Future Directions
Requirements
- A solid understanding of machine learning fundamentals
- Proficiency in Python programming
- Familiarity with pre-trained models and their practical applications
Target Audience
- Data Scientists
- Machine Learning Engineers
- AI Researchers
