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 142 737 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 142 737 subscribers.

According to the latest data from 14 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -1 292 over the last 30 days and by -44 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 6.29%. Within the first 24 hours after publication, content typically collects 1.82% reactions from the total number of subscribers.
Post reach: On average, each post receives 8 976 views. Within the first day, a publication typically gains 2 595 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 15 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.

142 737

Subscribers

-4424 hours

-2007 days

-1 29230 days

8 976

Post views

~ 2 59524 hours

~ 3 64748 hours

6.29%

Engagement rate

~ 1

Posts per day

Ads index

beta

Posts Archive

142 711

Paper: http://proceedings.mlr.press/v78/dosovitskiy17a/dosovitskiy17a.pdf Code: https://github.com/carla-simulator/carla

142 711

CARLA: An Open Urban Driving Simulator Open-source simulator for autonomous driving

142 711

https://github.com/dsgiitr/graph_nets?fbclid=IwAR39aQbvMVhVxNCbQOoxkxdkYgnfd3tMuqzvWBqz048i8iE8Sjavcqbh56M

142 711

PyTorch Implementation and Explanation of Graph Representation Learning papers involving DeepWalk, GCN, GraphSAGE, ChebNet & GAT.

142 711

Paper link, Blog and code are at their Github page: https://xbpeng.github.io/projects/DeepMimic/index.html?fbclid=IwAR3SaHAM-nii9UcpBJsWH-w7swuJudV9ouC7Ige8iuZDF0lbBy4ThchKhTo

142 711

DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills

142 711

Article: https://www.microsoft.com/en-us/research/blog/zero-deepspeed-new-system-optimizations-enable-training-models-with-over-100-billion-parameters/ Github: https://github.com/microsoft/DeepSpeed

142 711

ZeRO & DeepSpeed: New system optimizations enable training models with over 100 billion parameters

142 711

http://introtodeeplearning.com/

142 711

Course from MIT 6.S191 "Introduction to Deep Learning". Methods and applications in game play, medicine, language, art, computer vision, robotics and more

142 711

This video is synthetic and was created using deep learning

142 711

https://ai.facebook.com/blog/-introducing-pytorch3d-an-open-source-library-for-3d-deep-learning/

142 711

Introducing PyTorch3D: An open-source library for 3D deep learning. PyTorch3D: Faster, flexible 3D deep learning research

142 711

https://towardsdatascience.com/end-to-end-machine-learning-from-data-collection-to-deployment-ce74f51ca203 If you want to play with the app and see how it looks like: 💻Demo: https://www.reviews.ai2prod.com/ Code: https://github.com/MarwanDebbiche/post-tuto-deployment

142 711

End to End Machine Learning: From Data Collection to Deployment. - Collect and scrape data with Scrapy / Selenium - Train a deep character CNN for (English) sentiment analysis using PyTorch - Build an interactive web app with Dash to serve the model in real-time - Put everything in Docker Compose - Deploy to AWS on a custom domain name

142 711

More than 200 NLP datasets - this is gold (last update 21.01.202) https://quantumstat.com/dataset/dataset.html and also Google provided dataset search tool for publicly available datasets: https://datasetsearch.research.google.com/

142 711

Paper: https://arxiv.org/pdf/2001.05613.pdf Project page: http://www.ynl.t.u-tokyo.ac.jp/research/vmocap-syn/ Dataset will be available publicly soon

142 711

Synergetic Reconstruction from 2D Pose and 3D Motion for Wide-Space Multi-Person Video Motion Capture in the Wild

142 711

Paper pdf: https://arxiv.org/pdf/2001.05201.pdf Github: https://wywu.github.io/projects/EBT/EBT.html Youtube: https://youtu.be/tNPuAnvijQk

142 711

Everybody’s Talkin’: Let Me Talk as You Want This paper presents a method to edit a target portrait footage by taking a sequence of audio as input to synthesize a photo-realistic video.