Computer Science and Programming

Open in Telegram

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 Italy86 Technologies & Applications813...

📈 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 142 667 subscribers, ranking 813 in the Technologies & Applications category and 86 in the Italy region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 142 667 subscribers.

According to the latest data from 15 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -1 289 over the last 30 days and by -46 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 6.44%. Within the first 24 hours after publication, content typically collects 1.85% reactions from the total number of subscribers.
Post reach: On average, each post receives 9 197 views. Within the first day, a publication typically gains 2 646 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 16 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.

142 667

Subscribers

-4624 hours

-2077 days

-1 28930 days

9 197

Post views

~ 2 64624 hours

~ 3 57748 hours

6.44%

Engagement rate

~ 1

Posts per day

Ads index

beta

Posts Archive

142 667

https://medium.com/ibm-watson/watson-studio-desktop-is-now-free-for-academia-f8b50a118370

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Watson Studio Desktop is now free for academia. All products in your charge for free: * Watson Studio Cloud * Watson Studio Local * Watson studio Desktop Just visit, register as a student or faculty, varify your account, install and enjoy with service. You'll get detailed information in below medium link

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Main site: https://chokkan.github.io/deeplearning/ Github repo: https://github.com/chokkan/deeplearning

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Introduction to Deep learning with flavor of Natural Language Processing(NLP) Course (Tokyo Institue of Technology) materials, demos and implementations are available. Enjoy with DL. Happy learning

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computervisionnews-february2019.pdf3.13 MB

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Computer Vision news magazine RSIP vision. February 2019. CV Application, Challenges, Projects

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Rules of Machine Learning: Best Practices for ML Engineering by Martin Zinkevich best practices in ML from around Google 👆

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rules_of_ml.pdf4.49 KB

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https://towardsdatascience.com/a-comprehensive-introduction-to-different-types-of-convolutions-in-deep-learning-669281e58215?fbclid=IwAR0MVWaTWiPO_dcYvfj3Y73H4S7hwcgEBtUlyshKYyuaGklwP7YHOaMImj0

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You are deep learning enthusiast and Covolutions are unseperable part of your projects. In this tutorial given comprehensive guideline all about convolutions: -> Convolution v.s. Cross-correlation -> Convolution in Deep Learning (single channel version, multi-channel version) -> 3D Convolution -> 1 x 1 Convolution -> Convolution Arithmetic -> Transposed Convolution (Deconvolution, checkerboard artifacts) -> Dilated Convolution (Atrous Convolution) -> Separable Convolution (Spatially Separable Convolution, Depthwise Convolution) -> Flattened Convolution -> Grouped Convolution -> Shuffled Grouped Convolution -> Pointwise Grouped Convolution

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https://github.com/kmario23/deep-learning-drizzle?fbclid=IwAR0Q9BtyvfFUApwIIHoWZm6sYx1V78--aUXSpbheMAMO9Yab6vQWEiNzTCU

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Deep Learning Drizzle Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these selected and exciting lectures!! GitHub by Marimuthu Kalimuthu

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Impractical Python Project.pdf24.87 MB

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Github repository: https://github.com/rlvaugh/Impractical_Python_Projects

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Impractical PythonProjects by Lee Vaughan 2018

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https://mlwhiz.com/blog/2018/12/17/text_classification/?utm_source=telegram.me&utm_medium=social&utm_campaign=mdtrue-silenttruethe-best-of-gan-pape&utm_content=25847208

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What Kagglers are mostly using for Text Classification?

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https://paperswithcode.com/sota

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This is a super cool resource: Papers With Code now includes 950+ ML tasks, 500+ evaluation tables (including SOTA results) and 8500+ papers with code. Probably the largest collection of NLP tasks I've seen including 140+ tasks and 100 datasets.

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Prediction based algorithms in infographics. Type, name, description, advantages and disadvantages