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Обновлено 2024-11-19 01:51:18 +03:00
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
Обновлено 2024-11-18 23:05:01 +03:00
Examples of using the Planetary Computer
Обновлено 2024-11-14 23:14:22 +03:00
12 Weeks, 24 Lessons, AI for All!
Обновлено 2024-11-11 22:55:47 +03:00
Notebooks and documentation for AI-for-Earth-managed datasets on Azure
Обновлено 2024-07-25 14:51:04 +03:00
Best Practices, code samples, and documentation for Computer Vision.
microsoft
azure
machine-learning
python
deep-learning
data-science
computer-vision
kubernetes
artificial-intelligence
object-detection
tutorial
jupyter-notebook
convolutional-neural-networks
image-classification
image-processing
operationalization
similarity
Обновлено 2023-10-18 19:13:00 +03:00
Get started learning C# with C# notebooks powered by .NET Interactive and VS Code.
Обновлено 2023-10-06 22:39:11 +03:00
Python SDK for the Microsoft Emotion API, part of Cognitive Services
Обновлено 2023-06-27 16:07:21 +03:00
Land Cover Mapping
Обновлено 2023-06-12 23:24:38 +03:00
MS MARCO(Microsoft Machine Reading Comprehension) is a large scale dataset focused on machine reading comprehension, question answering, and passage ranking. A variant of this task will be the part of TREC and AFIRM 2019. For Updates about TREC 2019 please follow This Repository Passage Reranking task Task Given a query q and a the 1000 most relevant passages P = p1, p2, p3,... p1000, as retrieved by BM25 a succeful system is expected to rerank the most relevant passage as high as possible. For this task not all 1000 relevant items have a human labeled relevant passage. Evaluation will be done using MRR
Обновлено 2023-06-12 21:21:58 +03:00
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Обновлено 2023-05-01 23:51:33 +03:00
Shared utility scripts for AI for Earth projects and team members
Обновлено 2023-04-27 04:58:37 +03:00
This is an API Framework for AI models to be hosted locally or on the AI for Earth API Platform (https://github.com/microsoft/AIforEarth-API-Platform).
Обновлено 2023-01-03 18:27:33 +03:00
A deep learning project in cooperation with the NOAA Marine Mammal Lab to detect & classify arctic seals in aerial imagery to understand how they’re adapting to a changing world.
Обновлено 2022-12-07 20:55:41 +03:00
Cloud-Scale Data for Spring Developers Quick Start Guide
Обновлено 2022-11-29 01:22:11 +03:00
Simple image classifier that introduces machine learning with Codespaces
Обновлено 2022-10-01 02:15:20 +03:00
This accelerator was built to provide developers with all of the resources needed to build a solution to find ideal replaceable parts comparing with its charecteristics for avoiding supplieir chain part procurement issue using Azure Synapse Analytics and Azure Machine Learning.
Обновлено 2022-08-16 18:57:18 +03:00
Repo for artifacts used for Azure Data Studio smoke tests
Обновлено 2022-05-04 02:45:33 +03:00
Land cover mapping of the Orinoquía region in Colombia, in collaboration with Wildlife Conservation Society Colombia. An #AIforEarth project
Обновлено 2021-03-13 01:26:16 +03:00
Sample code for Channel 9 Python for Beginners course
Обновлено 2020-07-13 19:33:16 +03:00
Getting Started with ADLA with R
Обновлено 2018-11-20 18:21:14 +03:00