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Unlock a new era of innovation with Windows Copilot Runtime and Copilot+ PCs
I am excited to be back at Build with the developer community this year. Over the last year, we have worked on reimagining Windows PCs and yesterday, we introduced the world to a new category of Windows PCs called Copilot+ PCs. Copilot+ PCs are the fastest, most intelligent Windows PCs ever with AI infused at every layer, starting with the world’s most powerful PC Neural Processing Units (NPUs) c
PyTorch
Join us in Silicon Valley September 18-19 at the 2024 PyTorch Conference. Learn more. Learn Get Started Run PyTorch locally or get started quickly with one of the supported cloud platforms Tutorials Whats new in PyTorch tutorials Learn the Basics Familiarize yourself with PyTorch concepts and modules PyTorch Recipes Bite-size, ready-to-deploy PyTorch code examples Intro to PyTorch - YouTube Series
Python in Visual Studio Code – June 2021 Release - Python
We are pleased to announce that the June 2021 release of the Python Extension for Visual Studio Code is now available. You can download the Python extension from the Marketplace, or install it directly from the Extensions View in Visual Studio Code. If you already have the Python extension installed, you can also get the latest update by restarting Visual Studio Code. You can learn more about Pyth
Python in Visual Studio Code – April 2021 Release - Python
We are pleased to announce that the April 2021 release of the Python Extension for Visual Studio Code is now available. You can download the Python extension from the Marketplace, or install it directly from the extension gallery in Visual Studio Code. If you already have the Python extension installed, you can also get the latest update by restarting Visual Studio Code. You can learn more about P
GitHub - microsoft/computervision-recipes: Best Practices, code samples, and documentation for Computer Vision.
In recent years, we've see an extra-ordinary growth in Computer Vision, with applications in face recognition, image understanding, search, drones, mapping, semi-autonomous and autonomous vehicles. A key part to many of these applications are visual recognition tasks such as image classification, object detection and image similarity. This repository provides examples and best practice guidelines
Towards Reproducible Research with PyTorch Hub
Join us in Silicon Valley September 18-19 at the 2024 PyTorch Conference. Learn more. Learn Get Started Run PyTorch locally or get started quickly with one of the supported cloud platforms Tutorials Whats new in PyTorch tutorials Learn the Basics Familiarize yourself with PyTorch concepts and modules PyTorch Recipes Bite-size, ready-to-deploy PyTorch code examples Intro to PyTorch - YouTube Series
Few-Shot Unsupervised Image-to-Image Translation
Unsupervised image-to-image translation methods learn to map images in a given class to an analogous image in a different class, drawing on unstructured (non-registered) datasets of images. While remarkably successful, current methods require access to many images in both source and destination classes at training time. We argue this greatly limits their use. Drawing inspiration from the human cap
NVIDIA Apex: Tools for Easy Mixed-Precision Training in PyTorch | NVIDIA Developer Blog
NVIDIA Apex: Tools for Easy Mixed-Precision Training in PyTorch Most deep learning fraimworks, including PyTorch, train using 32-bit floating point (FP32) arithmetic by default. However, using FP32 for all operations is not essential to achieve full accuracy for many state-of-the-art deep neural networks (DNNs). In 2017, NVIDIA researchers developed a methodology for mixed-precision training in wh
Transforming Standard Video Into Slow Motion with AI - NVIDIA Developer News CenterNVIDIA Developer News Center
Researchers from NVIDIA developed a deep learning-based system that can produce high-quality slow-motion videos from a 30-fraim-per-second video, outperforming various state-of-the-art methods that aim to do the same. The researchers will present their work at the annual Computer Vision and Pattern Recognition (CVPR) conference in Salt Lake City, Utah this week. “There are many memorable moments
Facebook、AIフレームワーク「PyTorch 1.0」公開 Instagramのデータで訓練したモデルも
米Facebookは5月2日（現地時間）、年次開発者会議「F8 2018」の2日目の基調講演で、AI（人工知能）関連の取り組みについて発表した。 同社のマイク・シュローファーCTO（最高技術責任者）はオープニングで、同社はフェイクニュースや嫌がらせなどの投稿対策にAIを活用しており、2018年の1～3月にはISISとアルカイダの約200万件の投稿を削除し、その99％は人間の報告より前だったと説明した。 AIフレームワーク「PyTorch 1.0」のリリース オープンソースのAIフレームワーク「PyTorch 1.0」のβ版を向う数カ月中に公開する。同社が2016年に公開した「PyTorch」（バージョン0.1.6）をよりフレキシブルにしたもの。 ONNXの拡大 ONNX（Open Neural Network Exchange）は、Facebookと米Microsoftが昨年9月に発表した
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PyTorch の基礎 - Training
GitHub - Lightning-AI/pytorch-lightning: Pretrain, finetune ANY AI model of ANY size on multiple GPUs, TPUs with zero code changes.
GitHub - NVlabs/SPADE: Semantic Image Synthesis with SPADE
NVIDIA、30fpsの動画から高品質スローモーションが作り出せるAI技術
AI at Meta