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 Programming Italy87 Technologies & Applications811...

📈 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 140 414 subscribers, ranking 811 in the Technologies & Applications category and 87 in the Italy region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 140 414 subscribers.

According to the latest data from 02 September, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -753 over the last 30 days and by -1 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 8.00%. Within the first 24 hours after publication, content typically collects 2.00% reactions from the total number of subscribers.
Post reach: On average, each post receives 11 237 views. Within the first day, a publication typically gains 2 812 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 14.
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 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.

Buy Ad

140 414

Subscribers

-124 hours

-2397 days

-75330 days

11 237

Post views

~ 2 81224 hours

~ 3 85648 hours

8.00%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

140 414

Deep Learning lecture

140 414

https://glouppe.github.io/info8010-deep-learning/pdf/lec-all.pdf?fbclid=IwAR2V0VK9Bf8xlfFhj5aodBQcscH4T8qv9Xv-wN6h5QcbzhvAXW6N65XYLi0

140 414

Deep Learning lecture The full deck of (600+) slides, by Professor Gilles Louppe. PDF file available here:

140 414

https://kite.com/?fbclid=IwAR1TR47nDvN3ZE6Fdq-Oz63E8edbZ5GAGaBTBoNlhC2WIPvrKWp54C4m7hI

140 414

Faster in Python with Line-of-Code Completions. Machine-learning applied to programming in Python.

140 414

https://data-flair.training/blogs/python-tutorials-home/

140 414

Imrove your skills by Learning and Practicing Python, Machine Learning, Deep Learning (beginner, intermediate, advanced topic). Complete tutorial categorized series from data-flair

140 414

Translate in Google Sheets

140 414

Amazing Google Sheets feature. Did you know this?

140 414

https://github.com/Atcold/pytorch-Deep-Learning-Minicourse?fbclid=IwAR1C22kf_PRfJMzPn2oTNtceU8yw7_Zw46v43JBCva46W_sWv7o6IadHttk

140 414

Mini course in Deep Learning with PyTorch. Jupyter Notebook files and Slides also provided. Here is some content from repo: * ML and spiral classification, * CNN, * Salsa, * RNN, Word Language model, * Generative models, ........ * VAE, regularization Detailed explanation

140 414

https://github.com/Avik-Jain/100-Days-Of-ML-Code

140 414

"100 Days of Machine Learning" tutorial series with codes. Github repo. Some content example: * Data Preprocessing, * Simple Linear Regression, * Multiple Linear Regression, * Logistic Regression, * K nearest neighbours, * Math Behind Logistic, * Regression, * SVM, ......... * Digging Deeper| Mathplotlib |Pandas |Numpy, * Heirarchical Clustering Thanks for Avik Jain for sharing great tutorial

140 414

https://sites.google.com/view/deep-rl-bootcamp/lectures?fbclid=IwAR1jzH8D2adWGrAz2vQcz9N2gEh_BbIeckyWsLZZdFEEBmVYXmvi1Fnxl2c

140 414

Deep Reinforcement Learning Lectures series from Bootcamp. August 2017. Video materials and slides are provided. Berkeley CA

140 414

Good day dear subscribers. Today, 12th april, our channel is celebrating its 1 year birhtday and our community are already more than 10K. Within this past 1 year we learn or still learning more about specific topics through channel. I try with my best to provide, keep going with contemporary knowladge and practice, as well as, keep in touch with things based on #AI, #ML, #DL, #DS, #Python. Thanks for being with us and Stay with us. If you have suggestion to improve channel's content or related things, please let me know. Thanks

140 414

computervisionnews-april2019.pdf3.06 MB

140 414

Computer Vision news from RSIP VISION. April 2019

140 414

https://www.datacamp.com/community/blog/spacy-cheatsheet

140 414

The most important concepts and features of scaPy: Advanced NLP in Python