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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 142 711 subscribers, ranking 816 in the Technologies & Applications category and 87 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 142 711 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.

142 711
Subscribers
-4624 hours
-2077 days
-1 28930 days
Posts Archive
Amazing Google Sheets feature. Did you know this?

Mini course in Deep Learning with PyTorch. Jupyter Notebook files and Slides also provided. Here is some content from repo: * ML and spiral classification, * CNN, * Salsa, * RNN, Word Language model, * Generative models, ........ * VAE, regularization Detailed explanation

"100 Days of Machine Learning" tutorial series with codes. Github repo. Some content example: * Data Preprocessing, * Simple Linear Regression, * Multiple Linear Regression, * Logistic Regression, * K nearest neighbours, * Math Behind Logistic, * Regression, * SVM, ......... * Digging Deeper| Mathplotlib |Pandas |Numpy, * Heirarchical Clustering Thanks for Avik Jain for sharing great tutorial

Deep Reinforcement Learning Lectures series from Bootcamp. August 2017. Video materials and slides are provided. Berkeley CA

Good day dear subscribers. Today, 12th april, our channel is celebrating its 1 year birhtday and our community are already more than 10K. Within this past 1 year we learn or still learning more about specific topics through channel. I try with my best to provide, keep going with contemporary knowladge and practice, as well as, keep in touch with things based on #AI, #ML, #DL, #DS, #Python. Thanks for being with us and Stay with us. If you have suggestion to improve channel's content or related things, please let me know. Thanks

computervisionnews-april2019.pdf3.06 MB

Computer Vision news from RSIP VISION. April 2019
Computer Vision news from RSIP VISION. April 2019

The most important concepts and features of scaPy: Advanced NLP in Python

Play with #GAN(Generative Adversarial Networks) in your browser and better understand what's going on inside network
Play with #GAN(Generative Adversarial Networks) in your browser and better understand what's going on inside network

Data Science Project - Analyzing Space Launches with Python
Data Science Project - Analyzing Space Launches with Python

Well explained Tutorial series: Transfer Learning, Natural Language Processing, Text classification, etc from Sebastian Ruder.

Another great lecture series from Stanford. CS224N Natural Language Processing with Deep Learning | Winter 2019