Computer Science and Programming

前往频道在 Telegram

Channel specialized for advanced topics of: * Artificial intelligence, * Machine Learning, * Deep Learning, * Computer Vision, * Data Science * Python Admin: @otchebuch Memes: @memes_programming Ads: @Source_Ads, https://telega.io/c/computer_science

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网络:Computer Science and Programming 意大利87 技术与应用816...

📈 Telegram 频道 Computer Science and Programming 的分析概览

频道 Computer Science and Programming (@computer_science_and_programming) 英语语言赛道中的是活跃参与者。目前社区聚集了 142 737 名订阅者，在 技术与应用 类别中位列第 816，并在 意大利 地区排名第 87 位。

📊 受众指标与增长动态

自 невідомо 创建以来，项目保持高速增长，吸引了 142 737 名订阅者。

根据 14 六月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 -1 292，过去 24 小时变化为 -44，整体触达仍然可观。

认证状态： 未认证
互动率 (ER)： 平均受众互动率为 6.29%。内容发布后 24 小时内通常能获得 1.82% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 8 976 次浏览，首日通常累积 2 595 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 17。
主题关注点： 内容集中在 sellerflash, github, developer, pricing, waybienad 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台：
“Channel specialized for advanced topics of: * Artificial intelligence, * Machine Learning, * Deep Learning, * Computer Vision, * Data Science * Python Admin: @otchebuch Memes: @memes_programming Ads: @Source_Ads, https://telega.io/c/computer_sc...”

凭借高频更新（最新数据采集于 15 六月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。

142 737

订阅者

-4424 小时

-2007 天

-1 29230 天

8 976

帖子浏览量

~ 2 59524 小时

~ 3 64748 小时

6.29%

参与率

~ 1

每日帖子数

Ads index

beta

帖子存档

142 714

PyRetri: An open source deep learning based unsupervised image retrieval toolbox built on PyTorch🔥

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https://paperswithcode.com/conference/iclr-2020-1/official?fbclid=IwAR0IHbPMDciaoEtbFdVyqrNtkjCOWmL5vdevMdaONJl2-HkR_q1L4U26AtI

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ICLR 2020 (International Conference on Learning Representations) papers and their codes. Papers ranked based on stars

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CVPR 2020 Paper: https://arxiv.org/abs/2004.01180 Project Page: https://www.cmlab.csie.ntu.edu.tw/~yulunliu/ObstructionRemoval Github: https://github.com/alex04072000/ObstructionRemoval

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Learning to See Through Obstructions

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MIT Media Lab https://aieducation.mit.edu/resources

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AI Literacy for K-12 School Children

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3D Photography using Context-aware Layered Depth Inpainting

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Paper: https://drive.google.com/file/d/17ki_YAL1k5CaHHP3pIBFWvw-ztF4CCPP/view Github: https://github.com/vt-vl-lab/3d-photo-inpainting Project: https://shihmengli.github.io/3D-Photo-Inpainting/ Notebook: https://colab.research.google.com/drive/1706ToQrkIZshRSJSHvZ1RuCiM__YX3Bz

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Tasks under Unsupervised learning: https://www.infinitycodex.in/ Deep Unsupervised Learning: Lecture: https://youtu.be/JBb5sSC0JoY Slides: https://bit.ly/34dVGE1 Colab: https://bit.ly/2JEzsl1

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Important Tasks under Unsupervised Learning: Clustering Anomaly detection Dimensionality reduction and CS294-158 Deep Unsupervised Learning from Abbeel et al.

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Article link: https://arxiv.org/ftp/arxiv/papers/2002/2002.06177.pdf

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Gary Marcus Robust AI 17 February 2020

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The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence.

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https://vas3k.com/blog/machine_learning/?fbclid=IwAR3e3_Yy7LyEm7-Yqvuttx_zftCn5hL8NXBRSaut0gp7sT8AlxDbj-q3dNQ and find other free-resources: https://www.marktechpost.com/free-resources/?fbclid=IwAR2XJOTWGEdC9aFSRibXWZAZlLpJY9nmRnS_PDfNOgllc9o_mL3OxJaQleY

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Machine Learning for Everyone with great explanation

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Paper: https://arxiv.org/pdf/2003.05534.pdf Github: https://github.com/sniklaus/softmax-splatting Short Summary: https://www.marktechpost.com/2020/03/14/softmax-splatting-for-video-frame-interpolation/

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Softmax Splatting for Video Frame Interpolation (using Pytorch)

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https://www.marktechpost.com/free-ai-ml-deep-learning-video-lectures/?fbclid=IwAR0Yg2egB85ia-ZMxg_hQBghgVJFbc3Wf3s3qmWWlG-1I7QikH-ZDXA0Gn0

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Set of free AI, ML, Deep Learning, Reinforcement Learning, Computer Vision, Statistics video lectures collections(last updated 20th February 2020 with 140 items)