Multicomputation is one of the core ideas of the Wolfram Physics Project—and in particular is at the heart of our emerging understanding of quantum mechanics. But how can one get an intuition for what is initially the rather abstract idea of multicomputation? A good approach, I believe, is to see it in action in familiar systems and situations. And I explore here what seems like a particularly good example: games and puzzles.
In this post we will discuss apache parquet, an extremely efficient and well-supported file format. The post is geared towards data practitioners (ML, DE, DS) so we’ll be focusing on high-level concepts and using SQL to talk through core concepts, but links for further resources can be found throughout the post and in the comments.
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Design patterns are not just a way to structure code. They also communicate the problem addressed and how the code or component is intended to be used.
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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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