AI & ML · Advanced
Deep Learning & Generative AI Lab
Neural networks, CNNs, sequence models, transformers and practical generative AI — including retrieval-augmented assistants for research.
Course modules
- 01Neural network foundations in PyTorch
- 02CNNs for images and satellite scenes
- 03Sequence models and time-series forecasting
- 04Transformers, foundation models and fine-tuning
- 05Generative AI: prompting, RAG and evaluation
- 06Responsible AI, bias and deployment basics
Learning outcomes
- Train and evaluate deep models on real data
- Fine-tune a pretrained model for a domain task
- Build a grounded research assistant with RAG
- Understand limits, risks and evaluation of GenAI
Project-based training
Every participant completes a capstone: Responsible AI, bias and deployment basics. Labs use real environmental, agricultural or engineering datasets.
Tools
- PyTorch
- Hugging Face
- LangChain / LlamaIndex
- Colab
Who it’s for
Students and professionals with ML basics
Prerequisites
Python and introductory ML
