Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
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Updated
Jun 9, 2024 - Python
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
PDE discovery using UBIC (uncertainty-penalized Bayesian information criterion)
Uncertainty-penalized Bayesian information criterion (UBIC) for PDE Discovery
Uncertainty-penalized Bayesian information criterion (UBIC) for PDE Discovery
Uncertainty-penalized Bayesian information criterion (UBIC) for PDE Discovery
Regression model building and forecasting in R
EvalML is an AutoML library written in python.
Variable Selection with Knockoffs
An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
Fit and compare complex models reliably and rapidly. Advanced nested sampling.
Predviđanje rezultata telemarketinga
Bayesian X-ray analysis (nested sampling for Xspec and Sherpa)
This repository build to showcase a project on customer personality analysis. In this I have classified the customer into two clusters.
[ICML 2024] Selecting Large Language Model to Fine-tune via Rectified Scaling Law
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
[ICDE 2024] a Web-app for Evaluation of Model selection for Anomaly Detection in Time Series
Explore Mistral AI's extensive collection of models. Learn to select, prompt, and integrate Mistral's open-source and commercial models for tasks like classification, coding, and Retrieval Augmented Generation (RAG).
A project designing and evaluating the fairness of a predictive model for arrest decisions using the North Carolina Policing Dataset. The study compares logistic regression, KNN, and fine-tuned KNN to ensure high accuracy and fairness in predictions based on gender, age, and race.
A comprehensive library for machine learning and numerical computing. The library provides a set of tools for linear algebra, numerical computing, optimization, and enables a generic, powerful yet still efficient approach to machine learning.
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