Description
What is this course about?
Join instructor Soumya Batra as she outlines a practical, skills-focused approach to coding, equipping you with advanced techniques and strategies to peek inside the proverbial “black box” of language models. Through a series of guided exercises and demos, you’ll progress from visualizing basic attention patterns to more advanced scenarios such as implementing cutting-edge methods like sparse autoencoders and steering vectors. This course balances theoretical understanding with hands-on experience, giving you an opportunity to apply these techniques immediately to solve real-world AI development challenges.
Objectives
What will I be able to do by the end of this course?
- Differentiate interpretability from explainability in AI, identifying the most valuable scenarios for each technique.
- Apply and analyze attention map visualizations and sparse autoencoders to understand model internals.
- Implement and leverage steering vectors for practical applications, influencing model behavior.
- Train and evaluate your own sparse autoencoders to uncover meaningful concepts within transformer models.
- Utilize theoretical and hands-on knowledge to improve AI model transparency and efficacy in real-world scenarios.
Audience
Who is this course for?
- Machine learning engineers
- AI researchers
- Data scientists
- Technical professionals working with or developing language models