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Computer Science and Programming

Computer Science and Programming

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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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📈 Analytical overview of Telegram channel Computer Science and Programming

Channel Computer Science and Programming (@computer_science_and_programming) in the English language segment is an active participant. Currently, the community unites 138 788 subscribers, ranking 818 in the Technologies & Applications category and 89 in the Italy region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 138 788 subscribers.

According to the latest data from 03 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -962 over the last 30 days and by -16 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 5.14%. Within the first 24 hours after publication, content typically collects 1.66% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 7 136 views. Within the first day, a publication typically gains 2 310 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 12.
  • Thematic interests: Content is focused on key topics such as sellerflash, github, developer, pricing, waybienad.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“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...”

Thanks to the high frequency of updates (latest data received on 04 November, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

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138 788
Subscribers
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-3647 days
-96230 days
Posts Archive
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)

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 👇

Watson Studio Desktop is now free for academia. All products in your charge for free: * Watson Studio Cloud * Watson Studio Local * Watson studio Desktop Just visit, register as a student or faculty, varify your account, install and enjoy with service. You'll get detailed information in below medium link

Introduction to Deep learning with flavor of Natural Language Processing(NLP) Course (Tokyo Institue of Technology) materials, demos and implementations are available. Enjoy with DL. Happy learning

computervisionnews-february2019.pdf3.13 MB

Computer Vision news magazine RSIP vision. February 2019. CV Application, Challenges, Projects

Rules of Machine Learning: Best Practices for ML Engineering by Martin Zinkevich best practices in ML from around Google 👆
Rules of Machine Learning: Best Practices for ML Engineering by Martin Zinkevich best practices in ML from around Google 👆

rules_of_ml.pdf4.49 KB

You are deep learning enthusiast and Covolutions are unseperable part of your projects. In this tutorial given comprehensive guideline all about convolutions: -> Convolution v.s. Cross-correlation -> Convolution in Deep Learning (single channel version, multi-channel version) -> 3D Convolution -> 1 x 1 Convolution -> Convolution Arithmetic -> Transposed Convolution (Deconvolution, checkerboard artifacts) -> Dilated Convolution (Atrous Convolution) -> Separable Convolution (Spatially Separable Convolution, Depthwise Convolution) -> Flattened Convolution -> Grouped Convolution -> Shuffled Grouped Convolution -> Pointwise Grouped Convolution

Deep Learning Drizzle Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these selected and exciting lectures!! GitHub by Marimuthu Kalimuthu

Impractical Python Project.pdf24.87 MB

Impractical PythonProjects by Lee Vaughan 2018