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

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Learning perturbation sets for robust machine learning

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https://laconicml.com/

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One more great source of Data Science, Deep Learning, Machine Learning, Computer Vision, AI and more... Enjoy with hot topics and projects

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https://github.com/rasbt/deeplearning-models

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Up-to-date and detailed explanation of Deep Learning Models from Sebastian Raschka A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks. (80 Jupyter Notebook notes in total)

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https://arxiv.org/pdf/2003.11755.pdf

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Recently published Comprehensive survey about role of Deep Learning for Scientific discovery (March, 2020). Well structured information given from the authors by providing supplementary materials (Github code links). It worth to spend time to read.

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https://aihub.org/

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One more great website specialized to AI with News, Articles, Opinions, Tutorials, Resources and much more supported by Geeks of AI

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https://laconicml.com/free-artificial-intelligence-courses/?fbclid=IwAR2oQoGYdTqiQ6sOJC7LvJxpaeSVVBLwk6Fs7S-_p2iMDq-BqbrpqBUqUrc

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List some of the free Artificial Intelligence courses that come from Harvard University, MIT University, and Stanford University that anyone can attend, no matter where you live

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But, You can follow what happens there almost in real time: fill below link and receive every day during CVPR the official magazine CVPR Daily (16-17-18 June) - with all the highlights from CVPR, the Computer Vision and Pattern Recognition conference. https://www.rsipvision.com/feel-at-cvpr-as-if-you-were-at-cvpr/ Open Access version of papers are available at: http://openaccess.thecvf.com/CVPR2020.py

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It's CVPR time! We will not meet in person next week at CVPR 2020 Seattle: The conference has gone virtual...

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sticker.webp0.16 KB

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Github link of "AI DeOldify Tool": https://github.com/jantic/DeOldify?fbclid=IwAR2-glzM1UYuWHJWuNKi741bm8aeofZdE1-0v9ZN-6Lmlmh4ta63mn5ydZc —————————————————————————— Masked face recognition dataset👇 Wolrd’s most complete Masked Face Recognition Dataset is Free to Download: https://medium.com/the-programming-hub/wolrds-most-complete-masked-face-recognition-dataset-is-for-free-10d780eed512 ——————————————————————————

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You Can Color Your Grandparent's Old Pictures or Videos with AI DeOldify Tool

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Behind the Scene: Revealing the Secrets of Pre-trained Vision-and-Language Models Paper: https://arxiv.org/pdf/2005.07310.pdf Related Codes: https://github.com/airsplay/lxmert https://github.com/ChenRocks/UNITER

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Short over view of Artificial Neural Networks with examples Page: https://www.infinitycodex.in/

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Content-based Image retrieval Paper: https://arxiv.org/abs/2005.02154 Github: https://github.com/PyRetri/PyRetri Installation: https://github.com/PyRetri/PyRetri/blob/master/docs/INSTALL.md