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 142 711 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 142 711 名订阅者。

根据 15 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 -1 289，过去 24 小时变化为 -46，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 6.44%。内容发布后 24 小时内通常能获得 1.85% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 9 197 次浏览，首日通常累积 2 646 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 16 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。

142 711

订阅者

-4624 小时

-2077 天

-1 28930 天

9 197

帖子浏览量

~ 2 64624 小时

~ 3 57748 小时

6.44%

参与率

~ 1

每日帖子数

Ads index

beta

帖子存档

142 667

Amazing Google Sheets feature. Did you know this?

142 667

https://github.com/Atcold/pytorch-Deep-Learning-Minicourse?fbclid=IwAR1C22kf_PRfJMzPn2oTNtceU8yw7_Zw46v43JBCva46W_sWv7o6IadHttk

142 667

Mini course in Deep Learning with PyTorch. Jupyter Notebook files and Slides also provided. Here is some content from repo: * ML and spiral classification, * CNN, * Salsa, * RNN, Word Language model, * Generative models, ........ * VAE, regularization Detailed explanation

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https://github.com/Avik-Jain/100-Days-Of-ML-Code

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"100 Days of Machine Learning" tutorial series with codes. Github repo. Some content example: * Data Preprocessing, * Simple Linear Regression, * Multiple Linear Regression, * Logistic Regression, * K nearest neighbours, * Math Behind Logistic, * Regression, * SVM, ......... * Digging Deeper| Mathplotlib |Pandas |Numpy, * Heirarchical Clustering Thanks for Avik Jain for sharing great tutorial

142 667

https://sites.google.com/view/deep-rl-bootcamp/lectures?fbclid=IwAR1jzH8D2adWGrAz2vQcz9N2gEh_BbIeckyWsLZZdFEEBmVYXmvi1Fnxl2c

142 667

Deep Reinforcement Learning Lectures series from Bootcamp. August 2017. Video materials and slides are provided. Berkeley CA

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Good day dear subscribers. Today, 12th april, our channel is celebrating its 1 year birhtday and our community are already more than 10K. Within this past 1 year we learn or still learning more about specific topics through channel. I try with my best to provide, keep going with contemporary knowladge and practice, as well as, keep in touch with things based on #AI, #ML, #DL, #DS, #Python. Thanks for being with us and Stay with us. If you have suggestion to improve channel's content or related things, please let me know. Thanks