Training and fine-tuning Large Language Models (LLMs) (3 days)
This practical 3-day course teaches you to train and fine-tune Large Language Models for data analysis tasks. You'll learn the underlying principles of LLMs, explore techniques for optimizing model performance, and gain hands-on experience with TensorFlow and PyTorch. Whether you're a data analyst or machine learning enthusiast, you'll develop the skills to leverage LLMs to unlock new insights from your data.
Prerequisites
- A solid appreciation of AI technology
Contents
Introduction to Language Models and Data Analysis
- Overview of Language Models and their significance in data analysis
- Introduction to common data analysis tasks and challenges
- Ethical considerations and potential biases in LLM-based data analysis
Language Model Fundamentals and Training
- Fundamentals of language modeling and LLM architectures
- Pretrained LLMs and their applications in data analysis
- Data preprocessing and preparation for LLM training
- Hands-on exercise: Training an LLM using TensorFlow or PyTorch
Fine-Tuning LLMs for Data Analysis Tasks
- Techniques for fine-tuning LLMs for specific tasks
- Fine-tuning LLMs for text classification
- Fine-tuning LLMs for sentiment analysis
- Hands-on exercise: Fine-tuning an LLM for a data analysis task
Advanced Topics in LLMs for Data Analysis
- Transfer learning with LLMs: Leveraging pretrained models for new tasks
- Domain adaptation for LLMs: Adapting models to specific domains
- Multitask learning with LLMs: Training models for multiple related tasks
- Hands-on exercise: Applying advanced techniques to fine-tune LLMs
Evaluation, Interpretation, and Future Directions
- Metrics for evaluating LLM performance in data analysis
- Interpreting LLM outputs and internal representations
- Visualization techniques for LLM analysis
- Best practices for deploying and maintaining LLMs in production
- Future directions and emerging trends in LLM-based data analysis
