TGINSIGHT SIMILAR POSTS
Source channel @olddriverGDstudy · Post #10 · Mar 17
#语录 请大家做个素质狼友： 1 人和人需要的是相互尊重的，希望我们群的狼友能尊重老师。在相互尊重的情况下我相信大家会得到更好的体验。 2 请大家预约老师后如有变化应该尽快,提前的告知老师，因为老师每天的课时都是有限的。如果不提前告知也很可能再也约不到这位老师或者进入妹子们的黑名单。 3 请大家遵守行规（按照行规S了但是可以待够时间，享受下老师的服务和老师聊聊天。就算时间到了没S也算是课时结束了，如果第一次结束了又做第二次那么不管S没有都应该按PP付费。）,一般情况下P是60分钟 PP是90分钟 时间没到老师赶你走是老师的问题，但是超时就是狼友的问题，关于超时最好和老师协商一下，因为老师如果后面有学生，那么超时就会影响到后面的学生，很可能会给老师带来不必要的麻烦。如果想约PP的学生最好在预约的时候就给老师讲清楚。 4 关于等候的时间，有些时候有很多不可控因素比如学生迟到，学生学习时间长等因素，希望大家在等候的时候能稍微耐心点，个人感觉等候时间在20-30分钟还是可接受的。 5 希望我们群的兄弟都能做个素质狼友，当然我们也会对群里的各位老师有所要求，大家对老师有什么不满意的都可以在群里直接投诉，或者找管理员投诉。
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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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