TGINSIGHT SIMILAR POSTS
Source channel @olddriverGDstudy · Post #49 · Mar 24
江湖舔狗传 江湖者，江湖也！ 各兄弟五湖四海汇聚一堂，为的是个情字，讲的是个义字，说的是个道理。 江湖上无数前辈好汉，忍饥挨饿，夜以继日，通宵达旦，上下求索，陷过无数的坑，踏破无数双鞋换得了有限的几个极品资源，未曾敢占为己有，而是毫无保留，无私公布奉献。 这一切为什么？为的是天下草根、屌丝们，不受仙人跳之苦，不遭各种骗费之难，不枉花了辛苦搬砖的银两盘缠，这是多么高尚的精神，多么高贵的品质啊！ 江湖就是江湖，林子大了什么鸟儿都有，舔狗们也像病毒般出没，为害人间。这些禽兽毫无尊严、毫无底线，从溜须拍马、到阿谀奉承，从冷屁股到甜盘子全方位无死角。 舔狗，做着劝婊子从良的梦，抱着救风尘女子出火坑的“崇高”的性幻想，岂不知自己已是婊子口中的笑话！ 江湖有江湖的规矩，江湖有江湖的原则，江湖有江湖的风貌，江湖有江湖的脾气。 我知舔狗是死不光的，这一车死光了，下一车还在路上。 但舔狗永远不过是个道具而已，又何必自作多情。 舔狗，你听，电话声已响起，你的钟到了！闭上臭嘴，滚出去把门关上！ 作者：41秒哥 标签：#语录
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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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