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 & Applications816...

📈 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 142 737 subscribers, ranking 816 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 142 737 subscribers.

According to the latest data from 14 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -1 292 over the last 30 days and by -44 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 6.29%. Within the first 24 hours after publication, content typically collects 1.82% reactions from the total number of subscribers.
Post reach: On average, each post receives 8 976 views. Within the first day, a publication typically gains 2 595 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 15 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.

142 737

Subscribers

-4424 hours

-2007 days

-1 29230 days

8 976

Post views

~ 2 59524 hours

~ 3 64748 hours

6.29%

Engagement rate

~ 1

Posts per day

Ads index

beta

Posts Archive

142 730

List of top 200 deep learning Github repositories sorted by the number of stars.

142 730

Rotated Binary Neural Network Github (Pytorch implementation): https://github.com/lmbxmu/RBNN Paper: https://arxiv.org/abs/2009.13055

142 730

Binary Neural Network (BNN) is best feet for reducing the complexity of deep neural networks. But, it suffers severe performance degradation. Rotation based training leads to around 50% weight flips which maximize the information gain and showed state-of-the-arts in benchmark datasets Rotated Binary Neural Network (RBNN)

142 730

https://github.com/brinesoftwares/Flutter-AI-Rubik-cube-Solver?fbclid=IwAR0agon7_lmFDOoBeT3ru9skWvTer3syzLFz_vHDZy9isC3FzWTJJ3JufsE

142 730

AI based Rubik's Cube Solver using Flutter and Python

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https://www.nature.com/articles/s41586-020-2649-2

142 730

NumPy provides an easily readable, expressive, high-level API for array programming. It takes care of the underlying mechanics that make operations fast.

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https://dafriedman97.github.io/mlbook/content/table_of_contents.html And The list of Most Updated and Free Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Mathematics, Python Programming Resources. (Last Update: Sept 9, 2020): https://www.marktechpost.com/free-resources/?fbclid=IwAR0hc2qkxPMXhQGzsg07ffgFecRr01tSCRqlhb_XMR6PjPt1KNdy68cLy9w

142 730

Here is a new, and free book on Machine Learning from scratch. It includes the math and code examples. Solid reference.

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Organize the daily influx of ML content in meaningful ways without feeling overwhelmed, By Goku Mohandas et al. : https://madewithml.com/collections/

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Brian Huge, Antoine Savine : https://arxiv.org/abs/2005.02347 TF1 implementation notebook on Colab: https://colab.research.google.com/github/differential-machine-learning/notebooks/blob/master/DifferentialML.ipynb

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Differential Machine Learning

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FREE PDF download: https://d2l.ai/d2l-en.pdf Download the book in 'notebook' format to read and execute locally: https://d2l.ai/chapter_installation/index.html Website: https://d2l.ai Github: https://github.com/d2l-ai/d2l-en Article: https://www.amazon.science/latest-news/amazon-scientists-author-popular-deep-learning-book

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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.

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Classification library https://github.com/Tessellate-Imaging/monk_v1 Notebooks - https://github.com/Tessellate-Imaging/monk_v1/tree/master/study_roadmaps/4_image_classification_zoo Detection and Segmentation Library https://github.com/Tessellate-Imaging/ Monk_Object_Detection Notebooks: https://github.com/Tessellate-Imaging/Monk_Object_Detection/tree/master/application_model_zoo

142 730

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

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Blog: https://towardsdatascience.com/pp-yolo-surpasses-yolov4-object-detection-advances-1efc2692aa62 Github: https://github.com/PaddlePaddle/PaddleDetection

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

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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

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Learning perturbation sets for robust machine learning