Results for “path-normalization”

Fine-tune Cosmos Reason video QA models using supervised fine-tuning with FSDP parallelism, including dataset preparation, spec construction, and AutoML support.

Analyze whole-slide pathology images with Python: load 160+ slide formats, preprocess H&E stains, segment nuclei, construct spatial graphs, train ML models, and process multiplex immunofluorescence data (CODEX, Vectra).

Reduce high-dimensional data with UMAP for visualization, clustering preprocessing, and supervised or semi-supervised learning, including parameter tuning guidance.