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

📊 Audience metrics and dynamics

Since its creation on невідомо, 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.

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

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

NLP_2018_Highlights.pdf2.96 MB

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

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

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

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

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

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

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

TensorSpace is a neural network 3D visualization framework Built on TensorFlow.js, Three.js and Tween.js. Better understandin
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