Jefferson Building, Room 450, 17 Oxford Street, Cambridge, MA 02138
Instructors
Bret Nestor and Claudio Battiloro
Learning Outcomes
- Embed data and view t-SNEs of representations
- Embedding foundation models for image, text, and geospatial analysis
- Explore diminishing returns w.r.t. training data size
- Construct a basic RAG script
Use Cases
- Pure embeddings vs generative models
- Explore non-language Foundation Models
- Implement RAG systems for research
Prerequisites
- Familiarity with Python
- Basic understanding of vector spaces