This project integrates my earlier guide, "NLP Demystified: Exploring Prominent Models, Libraries, and Embedding Space," with practical applications for the Amazon KDD Cup 2022 competition on Improving Product Search.
Research logs, notes, and links for Professor Singh's 2023-2024 ERSP project.
Distance Metrics Detective Story – An interactive Jupyter notebook that explores when to use Cosine, Euclidean, Manhattan, Dot Product, and Hamming distances in vector search. Featuring hands‑on financial contracts dataset, visual comparisons, and a practical decision framework to help engineers select the right similarity measure
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