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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 140 431 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 140 431 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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140 431
Subscribers
-6424 hours
-2617 days
-77230 days
Posts Archive
BMW shares AI algorithms used in production, available on GitHub
BMW shares AI algorithms used in production, available on GitHub

Things you need to consider about your algorithm is working or not

Free AI Resources Find The Most Updated and Free Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Mathematics, Python Programming Resources. (Last Update: December 4, 2019)

RoboNet Project page: https://www.robonet.wiki/

RoboNet: A Dataset for Large-Scale Multi-Robot Learning (15 million video frames, 7 Robot platform).
RoboNet: A Dataset for Large-Scale Multi-Robot Learning (15 million video frames, 7 Robot platform).

Now t-SNE with RAPIDs cuML 2000x speed-up over scikit-learn. But is very slightly accurate (at most ~3% error). Here is also
Now t-SNE with RAPIDs cuML 2000x speed-up over scikit-learn. But is very slightly accurate (at most ~3% error). Here is also detailed explanation about t-SNE, wich nonlinear dimensionality reduction algorithm.

NVIDIA released a PyTorch library ‘Kaolin’, which in few steps, moves 3D models into neural networks.
NVIDIA released a PyTorch library ‘Kaolin’, which in few steps, moves 3D models into neural networks.

Here is the paper link: https://arxiv.org/pdf/1806.04558.pdf And unofficial implementation of this paper: https://github.com/CorentinJ/Real-Time-Voice-Cloning

South Korea has created an entire city for testing self-driving cars with 35 kinds of road test facilities. Here is the video from BuzzFeed news

Last week one of the important conference in computer vision fields ICCV19 held on Seoul and finished. Here is the voting results about bests. ICCV 2019 Best Papers: * Best Paper Award (Marr Prize): SinGAN: Learning a Generative Model from a Single Natural Image * Best Student Paper Award: PLMP — Point-Line Minimal Problems in Complete Multi-View Visibility * Best Paper Honorable Mentions Paper: Asynchronous Single-Photon 3D Imaging