We are open sourcing the industry's only universal catalog for data and AI! Other Unity Catalog updates announced at Data + AI Summit:
– GA of Lakehouse Monitoring
– Attribute-Based Access Controls
– Upcoming GA of Lakehouse Federation https://
bit.ly/4b8msOT
OPEN SOURCE
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Unity Catalog Open Source: Universal Data and AI Catalog
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Theano developers CUDA neural networks training infrastructure history
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I wouldn’t be surprised at all if for example one of the Theano developers like James Bergstra had been training neural nets on CUDA before me though. Software / infra was undervalued in academia and just not published nearly as much
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LevelDB Co-Authorship: Ghemawat and Developer Collaboration
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Interesting. One correction: Sanjay Ghemawat and I co-wrote the initial versions of LevelDB, not just me.
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Cohere’s Aya-23 multilingual model performance review
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https://
huggingface.co/spaces/CohereF
orAI/aya-23
… from @CohereForAI is supposed to excel in multi-lingual. Could you try and share what you think? -
Submit Your Favorite Research Papers for Community Feedback
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Friday is for research paper reading! Can you submit your favorite ones here and I'll check them out: https://
huggingface.co/papers/submit? It will give extra visibility to them thanks to @_akhaliq (we badly need more visibility for open research papers) and feedback from the community! -
Meta FAIR unveils groundbreaking AI models and research artifacts
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Exciting breakthrough in AI and open science! 🚀 @AIatMeta @Meta 's Fundamental AI Research (FAIR) team has just unveiled four groundbreaking AI models and crucial research artifacts, reinforcing their dedication to an open AI ecosystem and responsible innovation.
— Dr. Debashis Dutta (@debashis_dutta) 21 juin 2024
Here's… pic.twitter.com/a6GqE4HURXExciting breakthrough in AI and open science! @AIatMeta @Meta 's Fundamental AI Research (FAIR) team has just unveiled four groundbreaking AI models and crucial research artifacts, reinforcing their dedication to an open AI ecosystem and responsible innovation. Here's
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Meta FAIR Unveils Four Groundbreaking AI Models for Open Ecosystem
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Exciting breakthrough in AI and open science! 🚀 @AIatMeta @Meta 's Fundamental AI Research (FAIR) team has just unveiled four groundbreaking AI models and crucial research artifacts, reinforcing their dedication to an open AI ecosystem and responsible innovation.
— Dr. Debashis Dutta (@debashis_dutta) 21 juin 2024
Here's… pic.twitter.com/KRtdaYtJJ7Exciting breakthrough in AI and open science! @AIatMeta @Meta 's Fundamental AI Research (FAIR) team has just unveiled four groundbreaking AI models and crucial research artifacts, reinforcing their dedication to an open AI ecosystem and responsible innovation. Here's
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YOLOv10: Eliminating NMS and Advanced Object Detection Improvements
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In this video, we explore YOLOv10, a significant leap forward in the YOLO series that eliminates non-maximum suppression (NMS) and introduces several enhancements.https://t.co/d9uOfN2pS3
— Satya Mallick (@LearnOpenCV) 21 juin 2024
Take a deep dive into the implementation and advancements of YOLOv10.#yolov10… pic.twitter.com/NqpCxRgWTyIn this video, we explore YOLOv10, a significant leap forward in the YOLO series that eliminates non-maximum suppression (NMS) and introduces several enhancements. https://
youtube.com/watch?v=o0FuW3
1cveE
… Take a deep dive into the implementation and advancements of YOLOv10. #yolov10 -
Open-Source vs Closed-Source Generative AI Solutions
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Navigating The Generative AI Divide: Open-Source Vs. Closed-Source Solutions In this article, we explore the differences of open-source and proprietary tools, looking at the pros and cons of each when applying generative AI to organizations. https://
linkedin.com/pulse/navigati
ng-generative-ai-divide-open-source-vs-solutions-bernard-marr-tware/
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Micrograd: 94 Lines to Train Neural Networks
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These 94 lines of code are everything that is needed to train a neural network. Everything else is just efficiency. This is my earlier project Micrograd. It implements a scalar-valued auto-grad engine. You start with some numbers at the leafs (usually the input data and the