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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 ProgrammingItaly89Technologies & Applications817...

📈 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 138 820 subscribers, ranking 817 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 138 820 subscribers.

According to the latest data from 02 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -985 over the last 30 days and by -42 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 4.97%. Within the first 24 hours after publication, content typically collects 1.69% reactions from the total number of subscribers.
Post reach: On average, each post receives 6 896 views. Within the first day, a publication typically gains 2 346 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 11.
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 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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138 820

Subscribers

-4224 hours

-4077 days

-98530 days

6 896

Post views

~ 2 34624 hours

~ 3 42348 hours

4.97%

Engagement rate

No data

Posts per day

Ads index

Posts Archive

138 823

Github link of "AI DeOldify Tool": https://github.com/jantic/DeOldify?fbclid=IwAR2-glzM1UYuWHJWuNKi741bm8aeofZdE1-0v9ZN-6Lmlmh4ta63mn5ydZc —————————————————————————— Masked face recognition dataset👇 Wolrd’s most complete Masked Face Recognition Dataset is Free to Download: https://medium.com/the-programming-hub/wolrds-most-complete-masked-face-recognition-dataset-is-for-free-10d780eed512 ——————————————————————————

138 823

You Can Color Your Grandparent's Old Pictures or Videos with AI DeOldify Tool

138 823

Behind the Scene: Revealing the Secrets of Pre-trained Vision-and-Language Models Paper: https://arxiv.org/pdf/2005.07310.pdf Related Codes: https://github.com/airsplay/lxmert https://github.com/ChenRocks/UNITER

138 823

Short over view of Artificial Neural Networks with examples Page: https://www.infinitycodex.in/

138 823

Content-based Image retrieval Paper: https://arxiv.org/abs/2005.02154 Github: https://github.com/PyRetri/PyRetri Installation: https://github.com/PyRetri/PyRetri/blob/master/docs/INSTALL.md

138 823

PyRetri: An open source deep learning based unsupervised image retrieval toolbox built on PyTorch🔥

138 823

https://paperswithcode.com/conference/iclr-2020-1/official?fbclid=IwAR0IHbPMDciaoEtbFdVyqrNtkjCOWmL5vdevMdaONJl2-HkR_q1L4U26AtI

138 823

ICLR 2020 (International Conference on Learning Representations) papers and their codes. Papers ranked based on stars

138 823

CVPR 2020 Paper: https://arxiv.org/abs/2004.01180 Project Page: https://www.cmlab.csie.ntu.edu.tw/~yulunliu/ObstructionRemoval Github: https://github.com/alex04072000/ObstructionRemoval

138 823

Learning to See Through Obstructions

138 823

MIT Media Lab https://aieducation.mit.edu/resources

138 823

AI Literacy for K-12 School Children

138 823

3D Photography using Context-aware Layered Depth Inpainting

138 823

Paper: https://drive.google.com/file/d/17ki_YAL1k5CaHHP3pIBFWvw-ztF4CCPP/view Github: https://github.com/vt-vl-lab/3d-photo-inpainting Project: https://shihmengli.github.io/3D-Photo-Inpainting/ Notebook: https://colab.research.google.com/drive/1706ToQrkIZshRSJSHvZ1RuCiM__YX3Bz

138 823

Tasks under Unsupervised learning: https://www.infinitycodex.in/ Deep Unsupervised Learning: Lecture: https://youtu.be/JBb5sSC0JoY Slides: https://bit.ly/34dVGE1 Colab: https://bit.ly/2JEzsl1

138 823

Important Tasks under Unsupervised Learning: Clustering Anomaly detection Dimensionality reduction and CS294-158 Deep Unsupervised Learning from Abbeel et al.

138 823

Article link: https://arxiv.org/ftp/arxiv/papers/2002/2002.06177.pdf

138 823

Gary Marcus Robust AI 17 February 2020

138 823

The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence.

138 823

https://vas3k.com/blog/machine_learning/?fbclid=IwAR3e3_Yy7LyEm7-Yqvuttx_zftCn5hL8NXBRSaut0gp7sT8AlxDbj-q3dNQ and find other free-resources: https://www.marktechpost.com/free-resources/?fbclid=IwAR2XJOTWGEdC9aFSRibXWZAZlLpJY9nmRnS_PDfNOgllc9o_mL3OxJaQleY