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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:
Больше📈 Аналитический обзор Telegram-канала Computer Science and Programming
Канал Computer Science and Programming (@machinelearning_programming) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 14 843 подписчиков, занимая 8 736 место в категории Технологии и приложения и 29 532 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 14 843 подписчиков.
Согласно последним данным от 04 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -152, а за последние 24 часа — -7, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 14.63%. В первые 24 часа после публикации контент обычно набирает N/A% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 0 просмотров. В течение первых суток публикация набирает 0 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 0.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как learning, github, engineer, quantization, detection.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“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:”
Благодаря высокой частоте обновлений (последние данные получены 05 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.
14 843
Подписчики
-724 часа
-277 дней
-15230 день
Архив постов
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
.
5TH UG2+ PRIZE CHALLENGE CVPR 2022
$10K PRIZES
http://cvpr2022.ug2challenge.org/
https://docs.google.com/forms/d/e/1FAIpQLSeK0j4cPRNFQbm27qMfaTr27wRQ6tXMV2gmohjaJlbn2fAX0A/viewform
https://cmt3.research.microsoft.com/User/Login?ReturnUrl=%2FUG2CHALLENGE2022
invite your friends 🌹🌹
@MachineLearning_Programming
—————— ConvNeXt ——————--
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 🌹🌹
@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
Page: https://d2l.ai/
PyTorch based: https://d2l.ai/d2l-en-pytorch.pdf
MXNET based: https://d2l.ai/d2l-en.pdf
Github: https://github.com/d2l-ai/d2l-en
👉👉@MachineLearning_Programming
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
Page: https://d2l.ai/
PyTorch based: https://d2l.ai/d2l-en-pytorch.pdf
MXNET based: https://d2l.ai/d2l-en.pdf
Github: https://github.com/d2l-ai/d2l-en
👉👉@deeplearning_ai
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
+1HAYAI - 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
Deep Learning with Python: Neural Networks (complete tutorial)
https://towardsdatascience.com/deep-learning-with-python-neural-networks-complete-tutorial-6b53c0b06af0
@deeplearning_ai
👋 Welcome to @realgroupforprogrammer 👋
𝗟𝗲𝗮𝗿𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 👨💻
𝗟𝗲𝗮𝗿𝗻 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗛𝗮𝗰𝗸𝗶𝗻𝗴 🚀
𝗟𝗲𝗮𝗿𝗻 𝗕𝗹𝗮𝗰𝗸𝗛𝗮𝘁 𝗠𝗲𝘁𝗵𝗼𝗱𝘀 💙
𝗔𝗻𝗱 𝗺𝘂𝗰𝗵 𝗺𝗼𝗿𝗲 𝗹𝗮𝘁𝗲𝘀𝘁 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗺𝗲𝘁𝗵𝗼𝗱𝘀, 𝘁𝗶𝗽𝘀 𝗮𝗻𝗱 𝘁𝗿𝗶𝗰𝗸𝘀.
💻 𝗛𝗲𝗿𝗲 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗹𝗲𝗮𝗿𝗻 :- 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴, 𝗛𝗮𝗰𝗸𝗶𝗻𝗴, 𝗖𝗿𝗮𝗰𝗸𝗶𝗻𝗴, 𝗪𝗲𝗯 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁, 𝗔𝗽𝗽 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁, 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴, 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲, 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴, 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲, 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴, 𝗚𝗿𝗮𝗽𝗵𝗶𝗰 𝗱𝗲𝘀𝗶𝗴𝗻, 𝗔𝗻𝗶𝗺𝗮𝘁𝗶𝗼𝗻, 𝗩𝗶𝗱𝗲𝗼 𝗲𝗱𝗶𝘁𝗶𝗻𝗴, 𝗣𝗵𝗼𝘁𝗼𝗴𝗿𝗮𝗽𝗵𝘆, 𝗣𝗵𝗼𝘁𝗼𝘀 𝗲𝗱𝗶𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗺𝗮𝗻𝘆 𝗺𝗼𝗿𝗲 𝗹𝗼𝘁𝘀 𝗼𝗳 𝘁𝗵𝗶𝗻𝗴 𝗶𝗻 𝗳𝗿𝗲𝗲 📚🏅🎖
✅ 𝗔 𝗰𝗹𝗲𝗮𝗻 𝗹𝗶𝗯𝗿𝗮𝗿𝘆 𝗳𝗼𝗿 𝗴𝗲𝗲𝗸𝘀.
𝗚𝗲𝘁 𝗕𝘂𝗴 𝗕𝗼𝘂𝗻𝘁𝘆, 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝗶𝗻𝗴, 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗛𝗮𝗰𝗸𝗶𝗻𝗴, 𝗖𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 & 𝗹𝗼𝘁 𝗺𝗼𝗿𝗲 𝗹𝗮𝘁𝗲𝘀𝘁 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗯𝗮𝘀𝗲𝗱 𝗲𝗕𝗼𝗼𝗸𝘀.
𝗜𝗻 𝘁𝗵𝗶𝘀 𝗖𝗵𝗮𝗻𝗻𝗲𝗹, 𝗬𝗼𝘂 𝘄𝗶𝗹𝗹 𝗴𝗲𝘁 𝗨𝗱𝗲𝗺𝘆 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀, 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝗿𝗮 𝗖𝗼𝘂𝗿𝘀𝗲𝘀, & 𝗙𝗿𝗲𝗲𝗢𝗻𝗹𝗶𝗻𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀.
𝙁𝙤𝙧 𝙛𝙧𝙚𝙚 𝙘𝙤𝙪𝙧𝙨𝙚𝙨,𝙗𝙤𝙤𝙠𝙨,𝙥𝙧𝙤𝙟𝙚𝙘𝙩𝙨,𝙞𝙣𝙩𝙚𝙧𝙣𝙨𝙝𝙞𝙥𝙨,𝙥𝙡𝙖𝙘𝙚𝙢𝙚𝙣𝙩𝙨 𝙖𝙣𝙙 𝙟𝙤𝙗𝙨 𝙧𝙚𝙡𝙖𝙩𝙚𝙙 𝙢𝙖𝙩𝙚𝙧𝙞𝙖𝙡 𝙖𝙣𝙙 𝙪𝙥𝙙𝙖𝙩𝙚𝙨 𝙟𝙤𝙞𝙣 𝙤𝙪𝙧 𝙩𝙚𝙡𝙚𝙜𝙧𝙖𝙢 𝙘𝙝𝙖𝙣𝙣𝙚𝙡:
https://telegram.me/realgroupforprogrammer
𝗦𝗼 𝘄𝗵𝗮𝘁 𝗮𝗿𝗲 𝘆𝗼𝘂 𝘄𝗮𝗶𝘁𝗶𝗻𝗴 𝗳𝗼𝗿?
𝗝𝗼𝗶𝗻 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄👍
https://telegram.me/realgroupforprogrammer
Class Activation Map methods implemented in Pytorch
https://github.com/jacobgil/pytorch-grad-cam
invite your friends 🌹🌹
@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. It’s Free to sign up at https://www.ainews.com/newsletter/.
GIRAFFE: A Closer Look at the Code for CVPR 2021’s Best Paper
GIRAFFE is a learning-based, fully differentiable rendering engine for composing scenes as the summation of multiple “feature fields.”
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 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.
مجموعة مهمة الافضل ١٥ ورقة غش في مجال التعلم الآلي.
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
