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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 140 416 subscribers, ranking 811 in the Technologies & Applications category and 88 in the Italy region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 140 416 subscribers.

According to the latest data from 01 September, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -772 over the last 30 days and by -64 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 8.15%. Within the first 24 hours after publication, content typically collects 1.97% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 11 442 views. Within the first day, a publication typically gains 2 771 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 02 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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140 416
Subscribers
-6424 hours
-2617 days
-77230 days
Posts Archive
Differential Machine Learning
Differential Machine Learning

Dive Into Deep Learning August 2020 and FREE version!!! D2L is the 987-page book that Amazon scientists have compiled over th
Dive Into Deep Learning August 2020 and FREE version!!! D2L is the 987-page book that Amazon scientists have compiled over the past two years and has finally been completed... an interactive and ' open source book ' with code, math and discussions. What makes this book unique is that it was created with Jupyter Notebook and with the idea of ′′ Learning with Practice "... that is, the book in its entirety consists of executable code with adaptations in PyTorch, TensorFlow and MXNet.

80+ Jupyter Notebook tutorials on image classification, object detection and image segmentation in various domains 📌 Agricul
80+ Jupyter Notebook tutorials on image classification, object detection and image segmentation in various domains 📌 Agriculture and Food 📌 Medical and Healthcare 📌 Satellite 📌 Security and Surveillance 📌 ADAS and Self Driving Cars 📌 Retail and E-Commerce 📌 Wildlife

Baidu publishes PP-YOLO and pushes the state of the art in object detection research.
Baidu publishes PP-YOLO and pushes the state of the art in object detection research.

Tackled the problem of defining a perturbation set for real-world perturbations which cannot be easily described with a set of equations. Paper: https://arxiv.org/abs/2007.08450 Blog post: https://locuslab.github.io/2020-07-20-perturbation/ Code: https://github.com/locuslab/perturbation_learning

Learning perturbation sets for robust machine learning
Learning perturbation sets for robust machine learning

One more great source of Data Science, Deep Learning, Machine Learning, Computer Vision, AI and more... Enjoy with hot topics
One more great source of Data Science, Deep Learning, Machine Learning, Computer Vision, AI and more... Enjoy with hot topics and projects

Up-to-date and detailed explanation of Deep Learning Models from Sebastian Raschka A collection of various deep learning arch
Up-to-date and detailed explanation of Deep Learning Models from Sebastian Raschka A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks. (80 Jupyter Notebook notes in total)

Recently published Comprehensive survey about role of Deep Learning for Scientific discovery (March, 2020). Well structured information given from the authors by providing supplementary materials (Github code links). It worth to spend time to read.

One more great website specialized to AI with News, Articles, Opinions, Tutorials, Resources and much more supported by Geeks
One more great website specialized to AI with News, Articles, Opinions, Tutorials, Resources and much more supported by Geeks of AI

List some of the free Artificial Intelligence courses that come from Harvard University, MIT University, and Stanford Univers
List some of the free Artificial Intelligence courses that come from Harvard University, MIT University, and Stanford University that anyone can attend, no matter where you live

But, You can follow what happens there almost in real time: fill below link and receive every day during CVPR the official ma
But, You can follow what happens there almost in real time: fill below link and receive every day during CVPR the official magazine CVPR Daily (16-17-18 June) - with all the highlights from CVPR, the Computer Vision and Pattern Recognition conference. https://www.rsipvision.com/feel-at-cvpr-as-if-you-were-at-cvpr/ Open Access version of papers are available at: http://openaccess.thecvf.com/CVPR2020.py