前段时间一直被MajdataPlay的外键输入问题困扰：有玩家反映majplay会无征兆地出现拖判和吃音，但是内屏一切正常 因为我是第一次接触游戏开发，IO这方面也完全没经验 一开始我和bb本怀疑是线程调度的问题，即：IO线程时间片被其他线程挤占了，导致IO线程无法及时处理HID设备回报。为了验证这个猜想，我们尝试提高了IO线程的优先级，照旧 接下来我怀疑是我那套框架有问题：majplay是根据上一帧与这一帧的按键状态判断按键是不是"click"。为此我重写了这部分的实现，改进了IO线程与主线程之间的交互，问题照旧....... 到这里我已经怀疑这不是majplay的锅：IO线程没有任何异常，IO线程与主线程的交互没有问题，Note判定逻辑也没有问题，那就是设备确实没有回报给majplay或者设备发过来的回报中按键确实没有按下，但是大佬说hdd没有这种问题.....（人已经快崩溃了，这完全看不透也摸不着，因为我用单片机模拟玩家打高速纵连是完全没有问题的，我在家里用手台测试也没有问题） 到最后，bb本灵光一闪，说有没有可能是led刷新率过高，把按键控制板干爆炸了？我们让大佬把led刷新间隔从16ms改成100ms，吃音问题瞬间没有了，无语了 。。。。。。。。。。。。。。。。。。。。 adx是一个控制板同时管理按键和led，为什么我没有遇到吃音问题呢，因为我的手台不是adx的... #dev
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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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