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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 138 807 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 138 807 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.

Buy Ad

138 807

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

138 807

https://blog.floydhub.com/gentle-introduction-to-text-summarization-in-machine-learning/?fbclid=IwAR1fJn7if8K0CedBt6BlXv-LweDoIIglZwiKAkOQGswtiejyuDZaxEZZy-c

138 807

A Gentle Introduction to Text Summarization in Machine Learning from FLOYDHUB

138 807

https://github.com/lutzroeder/netron Supports: Mac OS, Windows, Linux, Browser version, Python Server

138 807

Netron is a viewer for neural network, deep learning and machine learning models.

138 807

https://github.com/rwightman/pytorch-image-models?fbclid=IwAR0QNx9Hui3Tucr04-yR5RlSXF9ApTNcXbMAilZrDnhFDiTy5QduNQQjgqA

138 807

PyTorch image models, scripts, pretrained weights -- (SE)ResNet/ResNeXT, DPN, EfficientNet, MobileNet-V3/V2/V1, MNASNet, Single-Path NAS, FBNet, and more

138 807

Detailed explanation at paper: 👇 https://arxiv.org/pdf/1905.10498.pdf and it's implementation and some results by using pytorch: 👇 https://github.com/facebookresearch/qmnist

138 807

MNIST reborn, restored and expanded. Now with an extra 50,000 training samples. If you used the original MNIST test set more than a few times, chances are your models overfit the test set. Time to test them on those extra samples. Now you will use #QMNIST instead of #MNIST

138 807

https://www.newworldai.com/

138 807

website, which will provide you all up-to-date and necessary information on Artificial Intelligence, Machine Learning, Deep Learning and some brain activities. You will also find TED Talks, Lectures and academic writings on these issues.

138 807

Speech2Face: Learning the Face behind a Voice #CVPR2019

138 807

ArXiv: 👇 https://arxiv.org/abs/1905.09773 Project: 👇 https://speech2face.github.io

138 807

Paper👇: https://arxiv.org/pdf/1905.08233.pdf Youtube video👇: https://www.youtube.com/watch?v=p1b5aiTrGzY&feature=youtu.be&fbclid=IwAR2Z1DgoIh_SdTuweiylm4L1aZpdSo8LV9v32XdivMcc3Q02mr7Qz1yUfwg

138 807

Samsung AI's latest work on #GAN to animate realistic head model sequences is setting the internet on fire

138 807

https://medium.com/deep-systems/supervisely-end-to-end-web-platform-for-deep-learning-and-computer-vision-eb8d239826d6?fbclid=IwAR1IAMJHDjLMIJOkj2GwoRzTsOkZmFm4LF62MedHXkrXmigzwr_m95ITIrU

138 807

Supervisely: end-to-end web-platform for Deep Learning and Computer Vision

138 807

https://www.adhiraiyan.org/deeplearning/02.00-Linear-Algebra?fbclid=IwAR1roZFCx4aHYDQfsbS47-su7VCajlQ_M9Z7ZHEatSx6xJI3CRUUoScwp2w

138 807

Linear Algebra topics you need to understand Deep Learning with practical examples in Tensorflow 2.0.

138 807

Machine learning is the main trend in IT and technologies .The most actual information about big data , machine learning , neural networks on channel : @ai_machinelearning_big_data

138 807

Detailed description and transcription of talk: http://blog.ezyang.com/