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86 技术与应用813...

📈 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 142 667 名订阅者，在 技术与应用 类别中位列第 813，并在 意大利 地区排名第 86 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 142 667 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。

142 667

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

https://ml-cheatsheet.readthedocs.io/en/latest/index.html

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Machine Learning Cheatsheet. Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more.

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https://github.com/zziz/pwc

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Papers with codes, which published in top conferences and sorted by stars. Read the paper and play with code. This repository is continuous progress and weekly update

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NLP_2018_Highlights.pdf2.96 MB

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NLP 2018 Highlights By Elvis Saravia. Summary of all the biggest NLP stories, state-of-the-art results and new interesting research directions of the year coming from both academia and the industry

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https://towardsdatascience.com/a-comprehensive-hands-on-guide-to-transfer-learning-with-real-world-applications-in-deep-learning-212bf3b2f27a

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A Comprehensive Hands-on Guide to Transfer Learning with Real-World Applications in Deep Learning

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Wonderfully interactive, gentle, and well done introduction to probability and statistics. Walk through this with your favorite kid and give them a head-start in life on ML https://seeing-theory.brown.edu/basic-probability/index.html

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https://sizovs.net/2018/12/17/stop-learning-frameworks/?fbclid=IwAR08Fey97XUeTica0ndl-hnxOxtMlkpd-TUNI0w6Di7BmXVIk6UmoSHr6U0

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Some important discussion and effective learning method from specialists. I'll highly recommend to read this greate article

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https://jalammar.github.io/illustrated-bert/

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The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)

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https://deeplearning.mit.edu/?fbclid=IwAR3N9JgFth8HNIFKcJFiVIjPNw-aR8a7LuWsF1OPpmRpv_zqOEjk7aYySwE

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MIT Deep Learning courses list from scholars and video tutorials, lectures series

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https://github.com/afshinea/stanford-cs-229-machine-learning/tree/master/en

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Cheatsheets for each machine learning field and ultimate complition of concepts from Stanford CS. Updated (2018) and in pdf version

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https://tensorspace.org/index.html

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TensorSpace is a neural network 3D visualization framework Built on TensorFlow.js, Three.js and Tween.js. Better understanding and imagination of deep learning with visualization