I share my solution for the Otto Competition, scoring LB 0.601, using Reranker, Transformers and GRU

Data for the MTEB leaderboard

MTEB: Massive Text Embedding Benchmark

Neural architecture search with reinforcement learning and Monte Carlo Search Trees

Core Utilities for NVIDIA Merlin

Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation and works with PyTorch.

The merlin dataloader lets you rapidly load tabular data for training deep leaning models with TensorFlow, PyTorch or JAX

Solutions to Recommender Systems competitions

Merlin Systems provides tools for combining recommendation models with other elements of production recommender systems (like feature stores, nearest neighbor search, and exploration strategies) into end-to-end recommendation pipelines that can be served with Triton Inference Server.

NVIDIA Merlin is an open source library providing end-to-end GPU-accelerated recommender systems, from feature engineering and preprocessing to training deep learning models and running inference in production.

Merlin Models is a collection of deep learning recommender system model reference implementations

NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.

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Labs and demos for courses for GCP Training (http://cloud.google.com/training).

Code repository for capstone project

UNYSport is a react native iOS app with python backend, which enables user to register, create and join sport groups

Image classification with PyTorch for Histopathologic Cancer Detection

Implementing 3D Gans for object generation

Modul to extract hidden features from images via tensorflow hub and inception v3

a ride-sharing aggregator for iOS

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