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Computer Science and Programming

Computer Science and Programming

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Channel for who have a passion for - * Artificial Intelligence * Machine Learning * Deep Learning * Data Science * Computer vision * Image Processing * Research Papers * Related Courses and Ebooks With advertising offers contact:

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๐Ÿ“ˆ Analytical overview of Telegram channel Computer Science and Programming

Channel Computer Science and Programming (@machinelearning_programming) in the English language segment is an active participant. Currently, the community unites 14 843 subscribers, ranking 8 736 in the Technologies & Applications category and 29 532 in the India region.

๐Ÿ“Š Audience metrics and dynamics

Since its creation on ะฝะตะฒั–ะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 14 843 subscribers.

According to the latest data from 04 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -152 over the last 30 days and by -7 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 14.63%. Within the first 24 hours after publication, content typically collects N/A% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 0 views. Within the first day, a publication typically gains 0 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 0.
  • Thematic interests: Content is focused on key topics such as learning, github, engineer, quantization, detection.

๐Ÿ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
โ€œChannel for who have a passion for - * Artificial Intelligence * Machine Learning * Deep Learning * Data Science * Computer vision * Image Processing * Research Papers * Related Courses and Ebooks With advertising offers contact:โ€

Thanks to the high frequency of updates (latest data received on 05 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.

14 843
Subscribers
-724 hours
-277 days
-15230 days
Posts Archive
The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch. https://www.ritchieng.com/the-incredible-pytorch/ https://github.com/ritchieng/the-incredible-pytorch t.me/deeplearning_ai .

โ€”โ€”โ€”โ€”โ€”โ€” ConvNeXt โ€”โ€”โ€”โ€”โ€”โ€”-- Facebook propose ConvNeXt, a pure ConvNet model constructed entirely from standard ConvNet modules.
โ€”โ€”โ€”โ€”โ€”โ€” ConvNeXt โ€”โ€”โ€”โ€”โ€”โ€”-- Facebook propose ConvNeXt, a pure ConvNet model constructed entirely from standard ConvNet modules. ConvNeXt is accurate, efficient, scalable and very simple in design. Github: https://github.com/facebookresearch/ConvNeXt Paper: https://arxiv.org/abs/2201.03545 invite your friends ๐ŸŒน๐ŸŒน @MachineLearning_Programming

An important collection of the 15 best machine learning cheat sheets. 1- Supervised Learning https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf 2- Unsupervised Learning https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-unsupervised-learning.pdf 3- Deep Learning https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-deep-learning.pdf 4- Machine Learning Tips and Tricks https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-machine-learning-tips-and-tricks.pdf 5- Probabilities and Statistics https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf 6- Comprehensive Stanford Master Cheat Sheet https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/super-cheatsheet-machine-learning.pdf 7- Linear Algebra and Calculus https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf 8- Data Science Cheat Sheet https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf 9- Keras Cheat Sheet https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Keras_Cheat_Sheet_Python.pdf 10- Deep Learning with Keras Cheat Sheet https://github.com/rstudio/cheatsheets/raw/master/keras.pdf 11- Visual Guide to Neural Network Infrastructures http://www.asimovinstitute.org/wp-content/uploads/2016/09/neuralnetworks.png 12- Skicit-Learn Python Cheat Sheet https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf 13- Scikit-learn Cheat Sheet: Choosing the Right Estimator https://scikit-learn.org/stable/tutorial/machine_learning_map/ 14- Tensorflow Cheat Sheet https://github.com/kailashahirwar/cheatsheets-ai/blob/master/PDFs/Tensorflow.pdf 15- Machine Learning Test Cheat Sheet https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/ https://t.me/MachineLearning_Programming

Dive into Deep Learning Interactive deep learning book with code, math, and discussions Implemented with NumPy/MXNet, PyTorch
Dive into Deep Learning Interactive deep learning book with code, math, and discussions Implemented with NumPy/MXNet, PyTorch, and TensorFlow Adopted at 300 universities from 55 countries @MachineLearning_Programming

Dive into Deep Learning Interactive deep learning book with code, math, and discussions Implemented with NumPy/MXNet, PyTorch
Dive into Deep Learning Interactive deep learning book with code, math, and discussions Implemented with NumPy/MXNet, PyTorch, and TensorFlow Adopted at 300 universities from 55 countries @deeplearning_ai

HAYAI - Artificial Intelligence in the Water Industry https://lnkd.in/dpWdyKin Please Vote for this project - it's a matter of One-Click. https://aiqom.ai/dashboard/challenge-project/6 This project participates in the Certified AI Entrepreneur (CAIE) Program provided by AIQOM and Khalifa Fund for Enterprise Development

HAYAI - Artificial Intelligence in the Water Industry https://lnkd.in/dpWdyKin Please Vote for this project - it's a matter o
+1
HAYAI - Artificial Intelligence in the Water Industry https://lnkd.in/dpWdyKin Please Vote for this project - it's a matter of One-Click. https://aiqom.ai/dashboard/challenge-project/6 This project participates in the Certified AI Entrepreneur (CAIE) Program provided by AIQOM and Khalifa Fund for Enterprise Development

๐Ÿ‘‹ Welcome to @realgroupforprogrammer ๐Ÿ‘‹ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป ๐—ฃ๐—ฟ๐—ผ๐—ด๐—ฟ๐—ฎ๐—บ๐—บ๐—ถ๐—ป๐—ด ๐Ÿ‘จโ€๐Ÿ’ป ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป ๐—˜๐˜๐—ต๐—ถ๐—ฐ๐—ฎ๐—น ๐—›๐—ฎ๐—ฐ๐—ธ๐—ถ๐—ป๐—ด ๐Ÿš€ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป ๐—•๐—น๐—ฎ๐—ฐ๐—ธ๐—›๐—ฎ๐˜ ๐— ๐—ฒ๐˜๐—ต๐—ผ๐—ฑ๐˜€ ๐Ÿ’™ ๐—”๐—ป๐—ฑ ๐—บ๐˜‚๐—ฐ๐—ต ๐—บ๐—ผ๐—ฟ๐—ฒ ๐—น๐—ฎ๐˜๐—ฒ๐˜€๐˜ ๐˜๐—ฒ๐—ฐ๐—ต๐—ป๐—ถ๐—ฐ๐—ฎ๐—น ๐—บ๐—ฒ๐˜๐—ต๐—ผ๐—ฑ๐˜€, ๐˜๐—ถ๐—ฝ๐˜€ ๐—ฎ๐—ป๐—ฑ ๐˜๐—ฟ๐—ถ๐—ฐ๐—ธ๐˜€. ๐Ÿ’ป ๐—›๐—ฒ๐—ฟ๐—ฒ ๐˜†๐—ผ๐˜‚ ๐—ฐ๐—ฎ๐—ป ๐—น๐—ฒ๐—ฎ๐—ฟ๐—ป :- ๐—ฃ๐—ฟ๐—ผ๐—ด๐—ฟ๐—ฎ๐—บ๐—บ๐—ถ๐—ป๐—ด, ๐—›๐—ฎ๐—ฐ๐—ธ๐—ถ๐—ป๐—ด, ๐—–๐—ฟ๐—ฎ๐—ฐ๐—ธ๐—ถ๐—ป๐—ด, ๐—ช๐—ฒ๐—ฏ ๐—ฑ๐—ฒ๐˜ƒ๐—ฒ๐—น๐—ผ๐—ฝ๐—บ๐—ฒ๐—ป๐˜, ๐—”๐—ฝ๐—ฝ ๐—ฑ๐—ฒ๐˜ƒ๐—ฒ๐—น๐—ผ๐—ฝ๐—บ๐—ฒ๐—ป๐˜, ๐— ๐—ฎ๐—ฐ๐—ต๐—ถ๐—ป๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด, ๐—”๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ถ๐—ฎ๐—น ๐—œ๐—ป๐˜๐—ฒ๐—น๐—น๐—ถ๐—ด๐—ฒ๐—ป๐—ฐ๐—ฒ, ๐——๐—ฒ๐—ฒ๐—ฝ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด, ๐——๐—ฎ๐˜๐—ฎ ๐—ฆ๐—ฐ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ, ๐——๐—ถ๐—ด๐—ถ๐˜๐—ฎ๐—น ๐— ๐—ฎ๐—ฟ๐—ธ๐—ฒ๐˜๐—ถ๐—ป๐—ด, ๐—š๐—ฟ๐—ฎ๐—ฝ๐—ต๐—ถ๐—ฐ ๐—ฑ๐—ฒ๐˜€๐—ถ๐—ด๐—ป, ๐—”๐—ป๐—ถ๐—บ๐—ฎ๐˜๐—ถ๐—ผ๐—ป, ๐—ฉ๐—ถ๐—ฑ๐—ฒ๐—ผ ๐—ฒ๐—ฑ๐—ถ๐˜๐—ถ๐—ป๐—ด, ๐—ฃ๐—ต๐—ผ๐˜๐—ผ๐—ด๐—ฟ๐—ฎ๐—ฝ๐—ต๐˜†, ๐—ฃ๐—ต๐—ผ๐˜๐—ผ๐˜€ ๐—ฒ๐—ฑ๐—ถ๐˜๐—ถ๐—ป๐—ด ๐—ฎ๐—ป๐—ฑ ๐—บ๐—ฎ๐—ป๐˜† ๐—บ๐—ผ๐—ฟ๐—ฒ ๐—น๐—ผ๐˜๐˜€ ๐—ผ๐—ณ ๐˜๐—ต๐—ถ๐—ป๐—ด ๐—ถ๐—ป ๐—ณ๐—ฟ๐—ฒ๐—ฒ ๐Ÿ“š๐Ÿ…๐ŸŽ– โœ… ๐—” ๐—ฐ๐—น๐—ฒ๐—ฎ๐—ป ๐—น๐—ถ๐—ฏ๐—ฟ๐—ฎ๐—ฟ๐˜† ๐—ณ๐—ผ๐—ฟ ๐—ด๐—ฒ๐—ฒ๐—ธ๐˜€. ๐—š๐—ฒ๐˜ ๐—•๐˜‚๐—ด ๐—•๐—ผ๐˜‚๐—ป๐˜๐˜†, ๐—ก๐—ฒ๐˜๐˜„๐—ผ๐—ฟ๐—ธ๐—ถ๐—ป๐—ด, ๐—˜๐˜๐—ต๐—ถ๐—ฐ๐—ฎ๐—น ๐—›๐—ฎ๐—ฐ๐—ธ๐—ถ๐—ป๐—ด, ๐—–๐˜†๐—ฏ๐—ฒ๐—ฟ๐˜€๐—ฒ๐—ฐ๐˜‚๐—ฟ๐—ถ๐˜๐˜†, ๐—ฃ๐—ฟ๐—ผ๐—ด๐—ฟ๐—ฎ๐—บ๐—บ๐—ถ๐—ป๐—ด & ๐—น๐—ผ๐˜ ๐—บ๐—ผ๐—ฟ๐—ฒ ๐—น๐—ฎ๐˜๐—ฒ๐˜€๐˜ ๐˜๐—ฒ๐—ฐ๐—ต๐—ป๐—ผ๐—น๐—ผ๐—ด๐˜† ๐—ฏ๐—ฎ๐˜€๐—ฒ๐—ฑ ๐—ฒ๐—•๐—ผ๐—ผ๐—ธ๐˜€. ๐—œ๐—ป ๐˜๐—ต๐—ถ๐˜€ ๐—–๐—ต๐—ฎ๐—ป๐—ป๐—ฒ๐—น, ๐—ฌ๐—ผ๐˜‚ ๐˜„๐—ถ๐—น๐—น ๐—ด๐—ฒ๐˜ ๐—จ๐—ฑ๐—ฒ๐—บ๐˜† ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€, ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐—ฟ๐—ฎ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€, & ๐—™๐—ฟ๐—ฒ๐—ฒ๐—ข๐—ป๐—น๐—ถ๐—ป๐—ฒ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€. ๐™๐™ค๐™ง ๐™›๐™ง๐™š๐™š ๐™˜๐™ค๐™ช๐™ง๐™จ๐™š๐™จ,๐™—๐™ค๐™ค๐™ ๐™จ,๐™ฅ๐™ง๐™ค๐™Ÿ๐™š๐™˜๐™ฉ๐™จ,๐™ž๐™ฃ๐™ฉ๐™š๐™ง๐™ฃ๐™จ๐™๐™ž๐™ฅ๐™จ,๐™ฅ๐™ก๐™–๐™˜๐™š๐™ข๐™š๐™ฃ๐™ฉ๐™จ ๐™–๐™ฃ๐™™ ๐™Ÿ๐™ค๐™—๐™จ ๐™ง๐™š๐™ก๐™–๐™ฉ๐™š๐™™ ๐™ข๐™–๐™ฉ๐™š๐™ง๐™ž๐™–๐™ก ๐™–๐™ฃ๐™™ ๐™ช๐™ฅ๐™™๐™–๐™ฉ๐™š๐™จ ๐™Ÿ๐™ค๐™ž๐™ฃ ๐™ค๐™ช๐™ง ๐™ฉ๐™š๐™ก๐™š๐™œ๐™ง๐™–๐™ข ๐™˜๐™๐™–๐™ฃ๐™ฃ๐™š๐™ก: https://telegram.me/realgroupforprogrammer ๐—ฆ๐—ผ ๐˜„๐—ต๐—ฎ๐˜ ๐—ฎ๐—ฟ๐—ฒ ๐˜†๐—ผ๐˜‚ ๐˜„๐—ฎ๐—ถ๐˜๐—ถ๐—ป๐—ด ๐—ณ๐—ผ๐—ฟ? ๐—๐—ผ๐—ถ๐—ป ๐—ฟ๐—ถ๐—ด๐—ต๐˜ ๐—ป๐—ผ๐˜„๐Ÿ‘ https://telegram.me/realgroupforprogrammer

Class Activation Map methods implemented in Pytorch https://github.com/jacobgil/pytorch-grad-cam invite your friends ๐ŸŒน๐ŸŒน @MachineLearning_Programming

Hello Everyone, A StartUp out of California is finally delivering AiNews to the masses. AiNews.com, well funded and will be delivering Ai News to a level not seen. Itโ€™s Free to sign up at https://www.ainews.com/newsletter/.

GIRAFFE: A Closer Look at the Code for CVPR 2021โ€™s Best Paper GIRAFFE is a learning-based, fully differentiable rendering engine for composing scenes as the summation of multiple โ€œfeature fields.โ€ https://towardsdatascience.com/giraffe-a-closer-look-at-cvpr-2021s-best-paper-1ec81f593fa9 https://t.me/MachineLearning_Programming

Join the channel of researchers and programmers, the channel includes a huge encyclopedia of programming books and scientific articles in addition to the most famous scientific projects t.me/datascience_books

Welcome to the Code Programmer community. Our community offers many software projects with source code attached to explanations about the codes In addition, we support both Arabic and English languages โ€‹โ€‹at the same time. https://t.me/CodeProgrammer

Artificial Intelligence && Deep Learning Channel for who have a passion for - * Artificial Intelligence * Machine Learning * Deep Learning * Data Science * Computer vision * Image Processing * Research Papers With advertising offers contact: @Muhammadyahyoo https://t.me/DeepLearning_ai

Channel for who have a passion for - * Artificial Intelligence * Machine Learning * Deep Learning * Data Science * Computer vision * Image Processing * Research Papers https://t.me/DeepLearning_ai

An important collection of the 15 best machine learning cheat sheets. ู…ุฌู…ูˆุนุฉ ู…ู‡ู…ุฉ ุงู„ุงูุถู„ ูกูฅ ูˆุฑู‚ุฉ ุบุด ููŠ ู…ุฌุงู„ ุงู„ุชุนู„ู… ุงู„ุขู„ูŠ. 1- Supervised Learning https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf 2- Unsupervised Learning https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-unsupervised-learning.pdf 3- Deep Learning https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-deep-learning.pdf 4- Machine Learning Tips and Tricks https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-machine-learning-tips-and-tricks.pdf 5- Probabilities and Statistics https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf 6- Comprehensive Stanford Master Cheat Sheet https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/super-cheatsheet-machine-learning.pdf 7- Linear Algebra and Calculus https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf 8- Data Science Cheat Sheet https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf 9- Keras Cheat Sheet https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Keras_Cheat_Sheet_Python.pdf 10- Deep Learning with Keras Cheat Sheet https://github.com/rstudio/cheatsheets/raw/master/keras.pdf 11- Visual Guide to Neural Network Infrastructures http://www.asimovinstitute.org/wp-content/uploads/2016/09/neuralnetworks.png 12- Skicit-Learn Python Cheat Sheet https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf 13- Scikit-learn Cheat Sheet: Choosing the Right Estimator https://scikit-learn.org/stable/tutorial/machine_learning_map/ 14- Tensorflow Cheat Sheet https://github.com/kailashahirwar/cheatsheets-ai/blob/master/PDFs/Tensorflow.pdf 15- Machine Learning Test Cheat Sheet https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/ @deeplearning_ai