Grid.ai
¿Qué es Grid.ai?
Grid.ai es una plataforma que se enfoca en el aprendizaje automático en lugar de la infraestructura. Su objetivo es democratizar la investigación de IA de vanguardia.
¿Cómo usar Grid.ai?
Para utilizar Grid.ai, simplemente regístrese en su sitio web y siga la documentación y tutoriales proporcionados.
Características principales de Grid.ai
Gestión de infraestructura de aprendizaje automático
Investigación de IA de vanguardia
Integración con PyTorch Lightning
Casos de uso de Grid.ai
Proyectos de investigación de IA
Entrenamiento y despliegue de modelos
Análisis y visualización de datos
FAQ de Grid.ai
¿Qué es Grid.ai?
¿Cómo utilizo Grid.ai?
Grid.ai Discordia
Aquí está el Discord de Grid.ai: https://discord.gg/XncpTy7DSt, https://discord.gg/MWAEvnC5fU. Para obtener más mensajes de Discord, haga clic aquí(/es/discord/xncpty7dst).
Grid.ai empresa
Grid.ai Nombre de la empresa: Grid.ai .
Youtube de Grid.ai
Enlace de Youtube de Grid.ai: https://www.youtube.com/c/PyTorchLightning
Linkedin de Grid.ai
Enlace de Linkedin de Grid.ai: https://www.linkedin.com/company/pytorch-lightning/
Twitter de Grid.ai
Enlace de Twitter de Grid.ai: https://twitter.com/LightningAI
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Analítica de Grid.ai
Grid.ai Análisis del tráfico del sitio web
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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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