Fine-tuned chemical language model for predicting molecular lipophilicity in drug design. Explores parameter-efficient fine-tuning strategies (LoRA, BitFit, IA3), layer freezing techniques, and influence-based data selection. Balances accuracy and computational efficiency for molecular property prediction tasks.
Case Study of the Google Data Analytics Capstone course. Analyzing Fitbit's dataset to understand how can a wellness company play it smart?
A practical introduction to Transformer fine-tuning, designed for participants of the International Olympiad in Artificial Intelligence – covering LoRA, Adapters, and the limitations of parameter-efficient methods.
Build a production‑grade, modular pipeline for fine‑tuning large language models with LoRA on domain‑specific tasks (e.g., legal QA, medical summarization, financial reasoning).
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