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

Network:Computer Science and Programming Italy87 Technologies & Applications816...

📈 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.

📊 Audience metrics and dynamics

Since its creation on невідомо, 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.

📝 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.

142 711

Subscribers

-4624 hours

-2077 days

-1 28930 days

9 197

Post views

~ 2 64624 hours

~ 3 57748 hours

6.44%

Engagement rate

~ 1

Posts per day

Ads index

beta

Posts Archive

142 697

Centre for Computational Statistics and Machine Learning from UCL's Machine Learning Summer School (MLSS'19) video lectures The topics range from optimization and Bayesian inference to deep learning, reinforcement learning, and Gaussian processes. The lectures are of tutorial style, starts from basics, but then quickly picking up the pace so that after 2-4 hours of teaching, they arrive at the state of the art in the subject area.

142 697

https://towardsdatascience.com/everything-you-need-to-know-about-tensorflow-2-0-b0856960c074

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Everything you need to know about TensorFlow 2.0

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More information available at MIT website: http://news.mit.edu/2019/ai-programming-gen-0626 Source code available at: https://probcomp.github.io/Gen/

142 697

MIT Release New AI Programming Language Called ‘GEN’. New AI programming language goes beyond deep learning. General-purpose language works for computer vision, robotics, statistics, and more.

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https://neurips.cc/Conferences/2019/CallForCompetitions?fbclid=IwAR1-Cr4kLXQqgd7wEVmIuPEleC4W7bASaTDfNc8VrjM7q7pWPBA0bvhuVvM

142 697

NeurIPS | 2019 Thirty-third Conference on Neural Information Processing Systems - Accepted Competitions

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YouTube link: https://youtu.be/4jROlXH9Nvc https://docs.google.com/presentation/d/1zyuwCx7knqnP-LJswlDfWSmk5FhFgFmYJGqdEZn8yhc/edit#slide=id.g33c734b530_0_656 Github link: https://github.com/scoutbee/pytorch-nlp-notebooks

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A Deep Dive into NLP with PyTorch. how to implement more advanced architectures and apply it to real world datasets.

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Platform for Machine Learning methods dependencies 3D visualization

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The platform for Machine Learning methods dependencies 3D visualization THE PROJECT IS AVAILABLE ONLINE: HTTPS://WWW.INFORNOPOLITAN.XYZ/BACKRONYM FOR MORE INFORMATION (medium): HTTPS://MEDIUM.COM/@ASADULAEVARIP/HOW-TO-GENERATE-IDEAS-IN-MACHINE-LEARNING-BDB9A7267392

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Amazing!! Deep Learning-based NLP techniques are going to revolutionize the way we write software. Here's Deep TabNine, a GPT-2 model trained on around 2 million files from GitHub. It becomes you lazy, as well as, helps you write code faster