Popular GitHub Repositories for Carbon Offsets

FedERA is a modular and fully customizable open-source FL framework, aiming to address these issues by offering comprehensive support for heterogeneous edge devices and incorporating both standalone and distributed computing. It includes new software modules to enhance usability and promote environ- mental sustainability.

? Awesome Image Generator for Code Snippets & Mockups

?????? Findto is an open source assistant for decentralized search. Explore the best of Web, AI, and more.

Using CNN-LSTM deep learning model for digital soil mapping. This is the code for paper "Zhang et al. A CNN-LSTM model for soil organic carbon content prediction with long time series of MODIS-based phenological variables"

SustainDC is a set of Python environments for Data Center simulation and control using Heterogeneous Multi Agent Reinforcement Learning. Includes customizable environments for workload scheduling, cooling optimization, and battery management, with integration into Gymnasium.

Implementation of EMNLP2020 accepted paper: "TopicBERT: Topic-aware BERT for Efficient Document Classification"

Implementation and evaluation of "FedZero: Leveraging Renewable Excess Energy in Federated Learning"

Chatbot for The Carbon Almanac book or a climate change related topic

? Greta is an agile voice assistant to help reduce your carbon footprint.

Energy Consumption-Aware Tabular Benchmark For Neural Architecture Search

Samples for fine-tuning HuggingFace models with AzureML

Large-scale digital mapping of soil organic carbon content by using machine learning algorithms

Prediksi tingkat emisi karbon di Purbalingga menggunakan Machine Learning berbasis data geospasial dari OpenStreetMap.

Learn to optimize machine learning tasks for environmental sustainability. Discover how to use real-time electricity data and low-carbon energy sources for model training and inference, reducing the carbon footprint of your cloud operations.

This Github repository contains a machine learning model that predicts the potability of water based on various parameters such as pH, hardness, solids, chloramines, sulfate, conductivity, organic carbon, trihalomethanes, turbidity, and potability. The model is built using Python and Scikit-learn libraries.

Maternal exposure to black carbon and nitrogen dioxide during pregnancy and birth weight: Using machine-learning methods to achieve balance in inverse-probability weights

Optimize AI prompts to reduce verbosity and carbon footprint with ScaleDown

Web-based files for location-based carbon footprint tracking using public available APIs.

This Project analyses the carbon footprint of the U.S. commercial sector using three machine learning models. A combination of energy consumption data and carbon dioxide emission data was used to achieve the carbon footprint variable.