#H5游戏#cocos#pixi#layabox H5游戏客户端工程师 薪资待遇：面议，依资历谈薪 简历投递窗口：@jiesi997@nownow168@tung51688 工作职责： 职责一：开发工作 任务1、使用Egret进行项目相关功能模块的开发； 任务2、根据项目需求，进行游戏程序设计及开发工作； 职责二：协调工作 任务1、与服务器后端工程师沟通设计网络通信协议等； 任务2、与项目组策划、美术人员共同讨论开发需求及设计游戏实现细节，保证产品质量和进度； 任职要求 1、5年以上相关工作经验，1年以上Egret、Layabox、Coocs2d-js、pixi等其中一种或多种引擎开发经验，egret引擎优先； 2、熱练掌握 Javascript/Typescript语言、es6 语法，良好的OOP编程思想，熱悉各种前端调试工具，熱悉js性能优化： 3、熱悉 canvas和webgl图形学原理，熟悉CSS布局规苑等前端常规知识; 4、熟悉WebSocket 和 HTTP/HTTPS等网络协议，精通常用数据结构和算法： 5、熟悉H5游戏性能优化，善于解决跨浏览器和移动设备兼容性问题； 6、具有良好的编码规范，善于思考，具有极强的学习能力和独立解决问题的能力，能对团队代码质量负责；
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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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