At Hugging Face, we are working on tackling various problems in open-source machine learning, including, hosting models securely and openly, enabling reproducibility, explainability and collaboration. We are thrilled to introduce you to our new library: Skops! With Skops, you can host your scikit-learn models on the Hugging Face Hub, create model cards for model documentation and collaborate with others.
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PyTorch is designed to be the framework that’s both easy to use and delivers performance at scale. Indeed it has become the most popular deep learning framework, by a mile among the research community. However, despite some lengthy official tutorials and a few helpful community blogs, it is not always clear what exactly has to be done to make your PyTorch training to work across multiple nodes.
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Stable Diffusion is a text-to-image latent diffusion model created by the researchers and engineers from CompVis, Stability AI and LAION. It is trained on 512x512 images from a subset of the LAION-5B database. LAION-5B is the largest, freely accessible multi-modal dataset that currently exists.
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This is the most step-by-step spelled-out explanation of backpropagation and training of neural networks. It only assumes basic knowledge of Python and a vague recollection of calculus from high school.
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This video presents our tutorial on Denoising Diffusion-based Generative Modeling: Foundations and Applications. This tutorial was originally presented at CVPR 2022 in New Orleans and it received a lot of interest from the research community.
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GPU architectures are critical to machine learning, and seem to be becoming even more important every day. However you can be an expert in machine learning without ever touching GPU code. It is a bit weird to be work always through abstraction.
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This is a collection of 16 tensor puzzles. Like chess puzzles these are not meant to simulate the complexity of a real program, but to practice in a simplified environment. Each puzzle asks you to reimplement one function in the NumPy standard library without magic.
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In this article, we explore the services of available cloud GPU platforms with a focus on relevant factors such as pricing, infrastructure, design, performance, support, and security. We use this to present the best platforms to consider for your cloud GPU necessities.
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PYSKL is a toolbox focusing on action recognition based on SKeLeton data with PYTorch. Various algorithms will be supported for skeleton-based action recognition. We build this project based on the OpenSource Project MMAction2.
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It’s often the case that we want to inspect intermediate layers of a model without modifying the code e.g. visualize attention matrices of language models, get values from an intermediate layer to feed to another layer, or applying a loss function to intermediate layers.
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Example Jupyter notebooks that demonstrate how to build, train, and deploy Hugging Face Transformers using Amazon SageMaker and the Amazon SageMaker Python SDK.
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