Grid.ai là một nền tảng tập trung vào học máy thay vì cơ sở hạ tầng. Nó nhằm mục đích gia dụng nghiên cứu AI hiện đại.
Để sử dụng Grid.ai, chỉ cần đăng ký trên trang web của họ và làm theo tài liệu và hướng dẫn được cung cấp.
Đây là mối bất hòa về Grid.ai: https://discord.gg/XncpTy7DSt, https://discord.gg/MWAEvnC5fU. Để biết thêm tin nhắn Discord, vui lòng nhấp vào đây(/vi/discord/xncpty7dst).
Grid.ai Tên công ty: Grid.ai .
Liên kết Youtube Grid.ai: https://www.youtube.com/c/PyTorchLightning
Liên kết Linkedin Grid.ai: https://www.linkedin.com/company/pytorch-lightning/
Liên kết Twitter Grid.ai: https://twitter.com/LightningAI
Được đăng vào Có thể 20 2024 bởi Adnan Rehan
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Entrena Yolov8 para Detección de Objetos con Cualquier Dataset | Python
#yolo #computervision #objectdetection #python #huggingface #ultralytics En este video te cuento paso a paso como entrenar un detector de objetos con tu cualquier dataset. 00:00:00 Inicio 00:00:18 Qué vamos a hacer 00:00:35 Cómo lo haremos 00:01:49 Exploración datatset (huggingface) 00:06:11 Grid.ai | Requerimientos | Descarga y preprocesa dataset 00:19:35 Ultralytics | Entrenamiento Yolov8 00:28:10 Exploración Resultados 00:30:00 Inferencia 🙏🏼 Donaciones: https://www.paypal.com/donate/?hosted_button_id=Z3MZDUM4VE5UC 💻 Código del video: https://github.com/FernandoLpz/YouTube/tree/main/ObjectTrackingYolov8_SORT ✅ Ultralytics: https://docs.ultralytics.com/ ¡Mis redes! 🤓 GitHub: https://github.com/FernandoLpz 🙂 https://medium.com/@ferneutron 🤠 LeetCode: https://leetcode.com/ferneutron/ 😎 Instagram: @ferneutronn 🥸 Twitter: @ferneutronn
You Can Really EARN +$1000! Big Recap of FREE Mining for PC & Phone - DePIN AI Chrome Extension 💯
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Sebastian Raschka, University of Wisconsin-Madison/ Lead AI Education, Grid.AI
Deep learning offers state-of-the-art results for classifying images and text. Common deep learning architectures and training procedures focus on predicting unordered categories, such as recognizing a positive and negative sentiment from written text or indicating whether images contain cats, dogs, or airplanes. However, in many real-world problems, we deal with prediction problems where the target variable has an intrinsic ordering. For example, think of customer ratings (e.g., 1 to 5 stars) or medical diagnoses (e.g., disease severity labels such as none, mild, moderate, and severe). This talk will describe the core concepts behind working with ordered class labels, so-called ordinal data. We will cover hands-on PyTorch examples showing how to take existing deep learning architectures for classification and outfit them with loss functions better suited for ordinal data while only making minimal changes to the core architecture. To view the slides, click here: https://fs.hubspotusercontent00.net/hubfs/2631050/Deep%20Learning%20Summit%20-%20Sebastian%20Raschka%20-%20UoW%20_%20Grid.pdf
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