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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 140 414 subscribers, ranking 811 in the Technologies & Applications category and 87 in the Italy region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 140 414 subscribers.

According to the latest data from 02 September, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -753 over the last 30 days and by -1 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 8.00%. Within the first 24 hours after publication, content typically collects 2.00% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 11 237 views. Within the first day, a publication typically gains 2 812 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 14.
  • 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 03 September, 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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140 414
Subscribers
-124 hours
-2397 days
-75330 days
Posts Archive
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

This is a great place to share resources with other, here is Global Artificial Community platform, where you can find questions, discussions solutions only about #AI. And also there are some upcoming great features, which will really usefull for us. Visit, login, share and be more related with #AI stuff.

Great article from Analytics Vidhya: "A Step-by-Step NLP Guide to Learn ELMo for Extracting Features from Text". * However you can utilize ElMo for several domains: - Machine Translation - Language Modeling - Text Summarization - Named Entity Recognition - Question-Answering Systems

Discover open source Deep Learning code and pretrained models. Deep learning researchers easily find pre-trained models for a variety of platforms and uses. Great resource

De Facto list of book from Geeks of Deep Learning. Happy learning from up-to-date resources. Updated list 2019
De Facto list of book from Geeks of Deep Learning. Happy learning from up-to-date resources. Updated list 2019

Let's travel zoopark of GANs. Here is listed more than 300 (~) types of GANs family for different purposes. Happy travel and enjoy

Interpretable Machine Learning "A Guide for Making Black Box Models Explainable" by Christoph Molnar
Interpretable Machine Learning "A Guide for Making Black Box Models Explainable" by Christoph Molnar