Code for Large Scale Hierarchical Text Classification competition. Final place: 3rd
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Updated
Oct 20, 2014 - C++
Code for Large Scale Hierarchical Text Classification competition. Final place: 3rd
A Monte Carlo and max-flow approach
Running field-aware factorization machines on the Criteo data
FashionAI Clothes Attribute Recognition
1st Place Solution for CrowdFlower Product Search Results Relevance Competition on Kaggle.
Home repository for the Regularized Greedy Forest (RGF) library. It includes original implementation from the paper and multithreaded one written in C++, along with various language-specific wrappers.
Implementing a handwritten digits recognition application on the ESP32-CAM dev board using Tensorflow Lite for Microcontrollers & PlatformIO
multi-GPU solver for traffic light scheduling problem (Hash Code 2021)
Google - Fast or Slow? Predict AI Model Runtime - Kaggle
This is my first Machine Learning Project. The project employs a variety of machine learning models, including Random Forests, Gradient Boosted Trees, and Neural Networks, to predict survival. Techniques for data cleaning, feature engineering, and model tuning are thoroughly documented in the Jupyter notebooks.
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
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