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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 138 792 subscribers, ranking 820 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 138 792 subscribers.

According to the latest data from 04 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -933 over the last 30 days and by -9 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 5.28%. 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 7 333 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 13.
  • 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 05 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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138 792
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-93330 days
Posts Archive
"Dive into Deep Learning" An interactive deep learning book for students, engineers, and researchers.

Datasets list by categories and description, which we can use for machine learning practices. Article originally written in Japanese language, but I hope in your browser "translate page" extension is turned on and you have no problem. Enjoy with playing with datasets in your research.

From command line into a GUI. YOLO demonstration running inside of a PySimpleGUI. Instruction is clearly given in project's github repository

"Advanced Deep Learning with Keras" by Rowel Atienza (Published october, 2018). I am providing this book's github repo for practicing directly with codes. You can learn about key points of Keras and series of GAN. I hope you enjoy

The "Python machine learning book 2nd edition" book code repository. With practical examples and provided Notebook file for convinience

All about GANs: Application area, performance, improvements, difficulties, issues, optimization, ...

Since is introduced by Ian Goodfellow (in 2014), GANs ( Generative Adversarial Networks) gained high attention among AI world and in challenging area of research, as well as, developed many frameworks and cool applications based GANs. Below We share link about list of fraweworks, which created and applied for certain research are based on GANs.

Jupyter Notebook is becoming most dominant IDE for many programming languages (especially, Python). In this link provided useful tips, tricks, and shortcuts about Jupyter Notebook

awesome machine learning Resources list (by language).

"Applied Deep Learning with Python" (august, 2018) GitHub repository