🚀 Exciting Project Announcement! 🚀 I'm thrilled to share that I’ve developed GitInvite – an open-source platform that makes collaborating on GitHub easier than ever! 🎉 💡 What is GitInvite? GitInvite allows users to generate secure GitHub repository invite links that can be shared with collaborators. No more manual collaborator additions! With just one link, you can grant access to your repos in a secure and efficient way. 🌟 Key Features: - Generate secure invite links to share repository access. - Cancel invite links anytime to prevent further use. - Revoke access from users who gained access via the link. - Easy collaboration for developers, teams, and open-source projects. 🎯 Beta Stage: GitInvite is currently in its beta stage, and I'm actively seeking feedback and suggestions for improvements. I would love to hear from the developer community to help shape the future of this tool! 💻 Want to try it out? You can access GitInvite here: https://gitinvite.vercel.app/ 🛠 Developers: The code is open-source, and I welcome contributions! Check out the GitHub repo here: https://github.com/rahulps1000/GitInvite Feel free to share your feedback, open issues, or contribute to the project! Let’s make GitHub collaboration even smoother together. 🙌 #GitHub#OpenSource#NextJS#GitInvite#Collaboration#Beta#WebDevelopment
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@repo_science · Post #3688 · 2023/11/03 12:00
#ML 😎 FREE RESOURCES TO LEARN MACHINE LEARNING Intro to ML by MIT Free Course Machine Learning for Everyone FREE BOOK ML Crash Course by Google Advanced Machine Learning with Python Github Practical Machine Learning Tools and Techniques Free Book Python Machine Learning for beginners ----- Main channel: @repo_science Coupons: @freecoupons_reposcience -----
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@repo_science · Post #3447 · 2023/07/17 15:14
#ML 🧠 Machine Learning Expert El aprendizaje automático es un vasto campo con muchos conceptos clave que conocer. Nuestro curso intensivo cubre todos los componentes básicos que necesita para sumergirse en el aprendizaje automático del mundo real. ✍️Ryan Doan | Ex-Amazon ML Infrastructure Engineer 🌐En 📆2022 🔗Link ----- Main channel:@repo_science Coupons:@freecoupons_reposcience -----
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@amneumarkt · Post #687 · 2025/07/18 06:24
#ml https://aryagxr.com/blogs/intro-to-nmc
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@amneumarkt · Post #682 · 2025/06/27 05:30
#ml Machine Learning Visualized — Machine Learning Visualized https://ml-visualized.com/?utm_source=substack&utm_medium=email
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@amneumarkt · Post #617 · 2024/08/25 14:03
#ml What’s Really Going On in Machine Learning? Some Minimal Models—Stephen Wolfram Writings https://writings.stephenwolfram.com/2024/08/whats-really-going-on-in-machine-learning-some-minimal-models/
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@amneumarkt · Post #607 · 2024/07/30 06:20
#ml Meta's second version of segment anything. https://github.com/facebookresearch/segment-anything-2 They have a nice demo: https://sam2.metademolab.com/
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@amneumarkt · Post #596 · 2024/07/07 20:53
#ml I was searching for a tool to visualize computational graphs and ran into this preprint. The hierarchical visualization idea is quite nice. https://arxiv.org/abs/2212.10774
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@amneumarkt · Post #595 · 2024/07/06 22:02
#ml Schmidhuber J. Deep Learning: Our Miraculous Year 1990-1991. In: arXiv.org [Internet]. 12 May 2020 [cited 7 Jul 2024]. Available: https://arxiv.org/abs/2005.05744
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@amneumarkt · Post #575 · 2024/05/09 19:51
#ml https://github.com/google-research/timesfm
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@amneumarkt · Post #538 · 2024/02/16 11:21
#ml Like a dictionary Kunc, Vladim’ir, and Jivr’i Kl’ema. 2024. “Three Decades of Activations: A Comprehensive Survey of 400 Activation Functions for Neural Networks.” arXiv [Cs.LG], February. http://arxiv.org/abs/2402.09092.
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@amneumarkt · Post #532 · 2024/02/09 05:35
#ml I got interested in satellite data last year and played with it a bit. It's fantastic. The spatiotemporal nature of it brings up a lot of interesting questions. Then I saw this paper today: Rolf, Esther, Konstantin Klemmer, Caleb Robinson, and Hannah Kerner. 2024. “Mission Critical -- Satellite Data Is a Distinct Modality in Machine Learning.” arXiv [Cs.LG], February. http://arxiv.org/abs/2402.01444.
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@amneumarkt · Post #531 · 2024/02/05 10:57
#ml Jelassi S, Brandfonbrener D, Kakade SM, Malach E. Repeat after me: Transformers are better than state space models at copying. arXiv [cs.LG]. 2024. Available: http://arxiv.org/abs/2402.01032 Not surprising at all when you have direct access to a long context. But hey, look at this title.
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