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

https://ai-pool.com/

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This is a great place to share resources with other, here is Global Artificial Community platform, where you can find questions, discussions solutions only about #AI. And also there are some upcoming great features, which will really usefull for us. Visit, login, share and be more related with #AI stuff.

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https://www.analyticsvidhya.com/blog/2019/03/learn-to-use-elmo-to-extract-features-from-text/

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Great article from Analytics Vidhya: "A Step-by-Step NLP Guide to Learn ELMo for Extracting Features from Text". * However you can utilize ElMo for several domains: - Machine Translation - Language Modeling - Text Summarization - Named Entity Recognition - Question-Answering Systems

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https://modelzoo.co/

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Discover open source Deep Learning code and pretrained models. Deep learning researchers easily find pre-trained models for a variety of platforms and uses. Great resource

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https://blog.floydhub.com/best-deep-learning-books-updated-for-2019/

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De Facto list of book from Geeks of Deep Learning. Happy learning from up-to-date resources. Updated list 2019

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https://deephunt.in/the-gan-zoo-79597dc8c347

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Let's travel zoopark of GANs. Here is listed more than 300 (~) types of GANs family for different purposes. Happy travel and enjoy

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https://christophm.github.io/interpretable-ml-book/index.html

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Interpretable Machine Learning "A Guide for Making Black Box Models Explainable" by Christoph Molnar

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https://github.com/CU-ITSS/Web-Data-Scraping-S2019

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Web data scraping with Python by Brian Keegan University of Colorado, 2019. Enjoy scraping web with detailed tutorial

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Now introducing Chrome extension that makes it easy to find code when browsing arxiv.org or Google Scholar:

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https://github.com/graykode/nlp-tutorial

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Natural Language Processing Tutorial for Deep Learning Researchers using Tensorflow and Pytorch Most of the models in NLP were implemented with less than 100 lines of code.(except comments or blank lines)

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Next decade of technologies and fields by interpretation of Business insider: 1 #AI 2 #IoT⌚️ 3 #blockchain ⛓ 4 3D print 🖨 5 mobile📱 6 autonomous cars 🚗 7 mobile internet 💻 8 robotics🤖 9 VR/AR 👓 10 wireless power🔌 11 quantum computing 🖥 12 5G 📡 13 voice assistant🎙 14 cybersecurity🔒 #MWC19 Video is credited from Tech Insider 👇