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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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πŸ“ˆ 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 142 667 subscribers, ranking 813 in the Technologies & Applications category and 86 in the Italy region.

πŸ“Š Audience metrics and dynamics

Since its creation on Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 142 667 subscribers.

According to the latest data from 15 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -1 289 over the last 30 days and by -46 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 6.44%. Within the first 24 hours after publication, content typically collects 1.85% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 9 197 views. Within the first day, a publication typically gains 2 646 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 17.
  • Thematic interests: Content is focused on key topics such as sellerflash, github, developer, pricing, waybienad.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
β€œChannel 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 16 June, 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.

142 667
Subscribers
-4624 hours
-2077 days
-1 28930 days
Posts Archive
Horizon: The first open source reinforcement learning platform for large-scale products and services

A Comprehensive Guide to Understand and Implement Text Classification in Python

Pytorch to Keras model convertor (beta version). pytorch2keras

Yandex (Russian search gaint) Data School: Course in Natural Language Processing github repoπŸ‘‡

Introducing AdaNet: Fast and Flexible AutoML from AI google blog

30 Free Courses: Neural Networks, Machine Learning, Algorithms, AI

7 steps to mastering Deep learning with Keras:

Implement a YOLO (v3) object detector from scratch in PyTorch

Both calculations and graphs at once from autodesk researchers. Here is a link with dataset and code. Paper also avaliable for more detail

Python Machine Learning Case Studies.pdf7.95 MB