ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
Обновлено 2024-11-20 05:08:56 +03:00
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Обновлено 2024-11-20 04:04:47 +03:00
TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data
Обновлено 2024-11-19 01:55:46 +03:00
HI-ML toolbox for deep learning for medical imaging and Azure integration
Обновлено 2024-11-18 20:06:15 +03:00
Detect and warn about suspicious IPs logging into Nextcloud
Обновлено 2024-11-18 04:42:49 +03:00
This is a showcase on Azure Cloud Native, the products, events and how to get started or go deep with cloud native technologies, including Serverless on Azure.
Обновлено 2024-11-15 21:42:57 +03:00
Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.
Обновлено 2024-11-13 06:41:06 +03:00
12 Weeks, 24 Lessons, AI for All!
Обновлено 2024-11-11 22:55:47 +03:00
Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
Обновлено 2024-11-09 13:45:59 +03:00
Batch Scoring For Deep Learning Models
Обновлено 2024-11-08 19:24:35 +03:00
Example models using DeepSpeed
Обновлено 2024-11-08 00:27:38 +03:00
Workflow that takes advantage of GATKs CNN tool which is a deep learning approach to filter variants based on Convolutional Neural Networks
Обновлено 2024-11-06 01:27:52 +03:00
Python notebooks with ML and deep learning examples with Azure Machine Learning Python SDK | Microsoft
Обновлено 2024-11-04 20:20:56 +03:00
This repo contains required files for the INTERSPEECH 2022 Audio Deep Packet Loss Concealment (PLC) Challenge.
Обновлено 2024-10-31 16:11:12 +03:00
Microsoft Finance Time Series Forecasting Framework (FinnTS) is a forecasting package that utilizes cutting-edge time series forecasting and parallelization on the cloud to produce accurate forecasts for financial data.
Обновлено 2024-10-29 17:51:56 +03:00
The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.
Обновлено 2024-10-28 14:18:34 +03:00
This package features data-science related tasks for developing new recognizers for Presidio. It is used for the evaluation of the entire system, as well as for evaluating specific PII recognizers or PII detection models.
Обновлено 2024-10-27 15:51:02 +03:00
Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.
Обновлено 2024-10-23 20:40:41 +03:00
Ongoing research training transformer language models at scale, including: BERT & GPT-2
Обновлено 2024-10-18 13:31:05 +03:00
Muzic: Music Understanding and Generation with Artificial Intelligence
Обновлено 2024-10-12 10:58:40 +03:00
A flexible and efficient deep neural network (DNN) compiler that generates high-performance executable from a DNN model description.
Обновлено 2024-09-19 09:31:52 +03:00
DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers.
Обновлено 2024-09-04 00:17:43 +03:00
A DNN inference latency prediction toolkit for accurately modeling and predicting the latency on diverse edge devices.
Обновлено 2024-07-31 00:16:53 +03:00
CoCosNet v2: Full-Resolution Correspondence Learning for Image Translation
Обновлено 2024-07-25 14:07:42 +03:00
This repo contains the scripts, models, and required files for the Deep Noise Suppression (DNS) Challenge.
Обновлено 2024-07-25 13:19:02 +03:00
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Обновлено 2024-07-03 13:54:08 +03:00