Обновлено 2024-11-20 00:21:07 +03:00
TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data
Обновлено 2024-11-19 01:55:46 +03:00
Best Practices on Recommendation Systems
azure
microsoft
machine-learning
python
deep-learning
kubernetes
data-science
artificial-intelligence
jupyter-notebook
tutorial
operationalization
ranking
rating
recommendation
recommendation-algorithm
recommendation-engine
recommendation-system
recommender
Обновлено 2024-11-18 12:48:34 +03:00
Keyring backend for Azure Artifacts
Обновлено 2024-11-04 20:29:32 +03:00
Sharing Updatable Models (SUM) on Blockchain
machine-learning
python
react
ai
ml
artificial-intelligence
node
economics
blockchain
ethereum
prediction-mar
prediction-market
smart-contracts
truffle
Обновлено 2024-10-31 16:38:58 +03:00
Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.
machine-learning
deep-learning
python
pytorch
hyperparameter-optimization
automated-machine-learning
automl
model-compression
nas
neural-architecture-search
darts
petridish
Обновлено 2024-10-23 20:40:41 +03:00
Public repository for Azure Support scripts and artifacts
Обновлено 2024-10-22 16:21:30 +03:00
Muzic: Music Understanding and Generation with Artificial Intelligence
Обновлено 2024-10-12 10:58:40 +03:00
The Microsoft SQL Server 3rd Party Backend for Django provides a connectivity layer for Django on SQL Server or Azure SQL DB.
Обновлено 2024-09-04 03:07:57 +03:00
ElectionGuard is a set of open source software components that can be used to create and publish end to end verifiable elections as well create a publishable artifact for ballot comparison audits.
Обновлено 2024-08-16 01:30:31 +03:00
Tools for detecting wildlife in aerial images using active learning
Обновлено 2024-07-25 14:23:22 +03:00
Planetary Computer SDK for Python
Обновлено 2024-07-15 20:30:17 +03:00
Collection of quickstart guides using a Raspberry Pi and Azure services
Обновлено 2024-06-18 04:28:07 +03:00
Multi-species bioacoustic classification using deep learning algorithms
Обновлено 2024-06-18 01:58:08 +03:00
Using machine learning to detect beluga whale calls in hydrophone recordings
Обновлено 2024-06-18 01:58:02 +03:00
Azure DevOps Extension for Azure CLI
azure
devops
azure-devops
git
cicd
azure-devops-extension
command-line
pipelines
azure-cli
azure-cli-extension
boards
command-line-tool
repos
agile
artifacts
Обновлено 2024-05-16 14:06:51 +03:00
Partner Center Azure CLI Extension
Обновлено 2024-04-08 22:07:45 +03:00
The Azure IoT Edge Dev Tool greatly simplifies your Azure IoT Edge development process. It has everything you need to get started and helps with your day-to-day Edge development.
Обновлено 2024-03-13 20:20:57 +03:00
This project provides an official implementation of our recent work on real-time multi-object tracking in videos. The previous works conduct object detection and tracking with two separate models so they are very slow. In contrast, we propose a one-stage solution which does detection and tracking with a single network by elegantly solving the alignment problem. The resulting approach achieves groundbreaking results in terms of both accuracy and speed: (1) it ranks first among all the trackers on the MOT challenges; (2) it is significantly faster than the previous state-of-the-arts. In addition, it scales gracefully to handle a large number of objects.
Обновлено 2023-10-04 00:42:58 +03:00
Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks.
machine-learning
artificial-intelligence
causality
domain-generalization
privacy-preserving-machine-learning
Обновлено 2023-10-03 07:31:52 +03:00
NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego
deep-learning
pytorch
natural-language-processing
artificial-intelligence
dnn
question-answering
model-compression
text-classification
knowledge-distillation
text-matching
qna
sequence-labeling
Обновлено 2023-07-22 06:07:54 +03:00
Python and F# libraries developed for biological computation as part of the Station-B project.
Обновлено 2023-07-07 01:46:46 +03:00
🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.
Обновлено 2023-06-13 00:30:31 +03:00
🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.
Обновлено 2023-06-13 00:30:31 +03:00
Python SDK for the Microsoft Language Understanding Intelligent Service API, part of Cognitive Services
Обновлено 2023-06-12 23:52:58 +03:00
Code for paper EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
Обновлено 2023-06-12 21:56:30 +03:00
Обновлено 2023-06-12 21:47:26 +03:00
Обновлено 2023-06-12 21:47:20 +03:00
Truly Conversational Search is the next logic step in the journey to generate intelligent and useful AI. To understand what this may mean, researchers have voiced a continuous desire to study how people currently converse with search engines. Traditionally, the desire to produce such a comprehensive dataset has been limited because those who have this data (Search Engines) have a responsibility to their users to maintain their privacy and cannot share the data publicly in a way that upholds the trusts users have in the Search Engines. Given these two powerful forces we believe we have a dataset and paradigm that meets both sets of needs: A artificial public dataset that approximates the true data and an ability to evaluate model performance on the real user behavior. What this means is we released a public dataset which is generated by creating artificial sessions using embedding similarity and will test on the original data. To say this again: we are not releasing any private user data but are releasing what we believe to be a good representation of true user interactions.
Обновлено 2023-06-12 21:21:58 +03:00