I will develop custom deep learning and nlp models

Want recommendations that actually reflect how your users behave not a static "people who bought X" table?

I build deep-learning recommendation and sequence models in Python: systems that learn from the order of user behavior to predict what someone will want next, the way production systems at Netflix and TikTok do.

What I build: Sequential recommenders (SASRec / GRU4Rec / transformer-based) Embedding & collaborative-filtering models NLP and sequence models for text and behavioral data Proper evaluation leave-one-out, Hit@K, NDCG, coverage & diversity (not inflated random-split metrics) An interactive demo app so you can see recommendations live

Recent work: I built a sequential movie recommender on the MovieLens 25M dataset implementing three models (Prod2Vec, GRU4Rec, SASRec) with a live demo that visualizes which past items drove each recommendation via attention weights. I've also shipped an end-to-end customer-analytics system that identified £403K of at-risk revenue for a retailer so I build models that tie to real business outcomes, not just leaderboard scores.

Tools: Python, PyTorch/TensorFlow, scikit-learn, pandas, Streamlit