Students build pre-calc graph intuition, then dive into CNN structure, fine-tuning workflows, and foundation model applications. Programming includes AI-assisted PyTorch work and an optional transformer overview if time allows.
Key Themes
- Pre-calc graph intuition for model behavior
- CNN architecture, fine-tuning, and applied use cases
- Foundation models and an optional transformer overview
Hands-on Moments
- Fine-tune a CNN on a small custom dataset.
- Use AI-assisted tools to accelerate PyTorch coding and debugging.
- Explore a lightweight transformer demo if time allows.
What Students Walk Away With
- Explain CNN structure and why fine-tuning works.
- Apply a foundation model to a targeted task.
- Use AI-assisted workflows to iterate faster and document choices.
Advanced Studio Deep learning labs with CNNs and fine-tuning.