A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Kaggle Notebooks

My Kaggle Github Repository

Numbers from the on-going war in Ukraine

This website provides a platform for users to predict their likelihood of developing diabetes based on various factors.

A set of resources around Neo4J ninjas program

This web application provides a comprehensive platform for testing and interacting with various AI agents developed for the ConnectX game. Built with a React frontend and a Python Flask backend, this platform offers a visually engaging interface that allows users to easily interact with and evaluate the performance of different game AI agents.

Daily consolidated and enriched snapshots of endoflife.date

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.

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python

This study focuses on four deep-learning models, which are Inception V3, MobileNet V2, ResNet152V2, and VGG19, aiming to enhance the accuracy of tumor Classification

This is a collection of my submissions to the ongoing Kaggle Playground Series!

All measures obtained since Diabetes Mellitus 1 diagnosis

No tf, no pytorch, just math using MNIST dataset

Empowering Data Driven insights through hands-on projects, SQL challenges and practical tools.

Llama3-8B-Finetune-and-RAG, code base for sample, implemented in Kaggle platform

Monkey Species Image dataset for fine-grain classification using a convolutional neural network

CIFAR-10 trained with CreateML and a SwiftUI app for real time model testing. Kaggle Score: 0.69660

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

Fetch all kernels written for competitions from Kaggle.