cuml

Rapidsai_Machine_learnring_on_GPU

The Incredible RAPIDS: a curated list of tutorials, papers, projects, communities and more relating to RAPIDS.

Building NVIDIA's RAPIDS (cuDF, cuML...) in Arch Linux

26th place solution from 3290 teams held on HackerEarth

Make AI coding agents write RAPIDS-first GPU Python.

GPU-based ML to classify Higgs boson signal from background in particle physics using RAPIDS framework

GPU-accelerated ML pipeline predicting Dota 2 Radiant wins from match data: hero picks, chat TF-IDF and gold/exp timeline features (RAPIDS cuML, out-of-time CV, Optuna)

This is a Phishing Classifier project in which model identify the phishing data using various machine learning techniques and help to prevent from malicious virus and cyber attack.

How to use GPU-accelerated tools to conduct data science faster, leading to more scalable, reliable, and cost-effective results.

Compare feature importances created between cuml and sklearn RandomForestClassifiers

This project predicts the "Scariest Monster" using a dataset of 12 million entries and 106 features. Utilizing GPU-accelerated processing and the Random Forest Regressor using the Nvidia Rapids API. The goal is to minimize RMSE for accurate predictions

combination of EvalML with Rapids for the WiDS 2021 competition

This project integrates NVIDIA's cuML library with scikit-learn to accelerate classical machine learning algorithms using GPU. It enables faster training and inference by leveraging RAPIDS for scalable, high-performance ML workflows.

A compilation of various ML and DL models and ways to optimize the their inferences.

Running KNN algorithm much faster on GPU for free using RAPIDS packages like cuML and cuDF

we took nearly 100000 teacher details for training and a small amount of them for testing where we used 1 for least recommended and 5 for most recommended as output for training data.

Códigos PYTHON. Pandas, Scikit-Learning, Rapids, Cuml, Cudf.

An end-to-end Machine Learning and Deep Learning pipeline for San Francisco Crime Classification using spatial-temporal features, RAPIDS cuml, and Optuna.

Performance benchmarking of CPU vs GPU implementations of K-Means clustering using scikit-learn, RAPIDS cuML, and Welch's t-Test across 12 datasets.

Proposed and implemented the NLP model to compute the semantic similarity between two english sentences/questions using the Support Vector Machine(SVM).