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Duration 14 hours
Course Outline
Introduction to Fine-Tuning
- Defining fine-tuning and its scope
- Key use cases and advantages of fine-tuning
- Overview of pre-trained models and transfer learning concepts
Preparing for Fine-Tuning
- Data collection and cleaning processes
- Identifying task-specific data requirements
- Conducting exploratory data analysis and preprocessing
Techniques for Fine-Tuning
- Leveraging transfer learning and feature extraction
- Fine-tuning transformers using Hugging Face
- Strategies for supervised versus unsupervised tasks
Fine-Tuning Large Language Models (LLMs)
- Adapting LLMs for NLP tasks (e.g., text classification, summarization)
- Training LLMs with custom datasets
- Guiding LLM behavior through prompt engineering
Optimization and Evaluation
- Hyperparameter tuning strategies
- Methods for evaluating model performance
- Mitigating overfitting and underfitting issues
Scaling Fine-Tuning Efforts
- Implementing fine-tuning on distributed systems
- Utilizing cloud-based solutions for scalability
- Case studies: Insights from large-scale fine-tuning projects
Best Practices and Challenges
- Best practices for ensuring fine-tuning success
- Navigating common challenges and troubleshooting
- Ethical considerations in fine-tuning AI models
Advanced Topics (Optional)
- Fine-tuning multi-modal models
- Exploring zero-shot and few-shot learning
- Investigating LoRA (Low-Rank Adaptation) techniques
Summary and Next Steps
Requirements
- 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
