TGINSIGHT SIMILAR POSTS
Source channel @githubtrending · Post #15035 · Aug 7
#dart You can watch live streams simply on multiple platforms like Huya, Douyu, Bilibili, and Douyin using an app called Simple Live. It works on Android, iOS, Windows, MacOS, Linux, and Android TV, though some versions are still in beta. The app is built with Flutter and includes features to get live video and chat messages (danmaku) from these sites. You need to compile the app yourself since no ready-made installer is provided. This gives you a lightweight, easy way to watch live broadcasts from popular Chinese streaming platforms on many devices without extra cost or ads. https://github.com/xiaoyaocz/dart_simple_live
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@repo_science · Post #3688 · 11/03/2023, 12:00 PM
#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 · 07/17/2023, 03:14 PM
#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 · 07/18/2025, 06:24 AM
#ml https://aryagxr.com/blogs/intro-to-nmc
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@amneumarkt · Post #682 · 06/27/2025, 05:30 AM
#ml Machine Learning Visualized — Machine Learning Visualized https://ml-visualized.com/?utm_source=substack&utm_medium=email
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@amneumarkt · Post #617 · 08/25/2024, 02:03 PM
#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 · 07/30/2024, 06:20 AM
#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 · 07/07/2024, 08:53 PM
#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 · 07/06/2024, 10:02 PM
#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 · 05/09/2024, 07:51 PM
#ml https://github.com/google-research/timesfm
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@amneumarkt · Post #538 · 02/16/2024, 11:21 AM
#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 · 02/09/2024, 05:35 AM
#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 · 02/05/2024, 10:57 AM
#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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