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Computer Science and Programming

Computer Science and Programming

Kanalga Telegramโ€™da oโ€˜tish

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:

Ko'proq ko'rsatish

๐Ÿ“ˆ Telegram kanali Computer Science and Programming analitikasi

Computer Science and Programming (@machinelearning_programming) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 14 846 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 8 736-o'rinni va Hindiston mintaqasida 29 532-o'rinni egallagan.

๐Ÿ“Š Auditoriya koโ€˜rsatkichlari va dinamika

ะฝะตะฒั–ะดะพะผะพ sanasidan buyon loyiha tez oโ€˜sib, 14 846 obunachiga ega boโ€˜ldi.

04 Iyun, 2026 dagi oxirgi maโ€™lumotlarga koโ€˜ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -152 ga, soโ€˜nggi 24 soatda esa -7 ga oโ€˜zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya oโ€˜rtacha 14.63% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining N/A% ini tashkil etuvchi reaksiyalarni toโ€˜playdi.
  • Post qamrovi: Har bir post oโ€˜rtacha 0 marta koโ€˜riladi; birinchi sutkada odatda 0 ta koโ€˜rish yigโ€˜iladi.
  • Reaksiyalar va oโ€˜zaro taโ€™sir: Auditoriya faol: har bir postga oโ€˜rtacha 0 ta reaksiya keladi.
  • Tematik yoโ€˜nalishlar: Kontent learning, github, engineer, quantization, detection kabi asosiy mavzularga jamlangan.

๐Ÿ“ Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida taโ€™riflaydi:
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€

Yuqori yangilanish chastotasi (oxirgi maโ€™lumot 05 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boโ€˜lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim taโ€™sir nuqtasiga aylantirishini koโ€˜rsatadi.

14 846
Obunachilar
-724 soatlar
-277 kunlar
-15230 kunlar
Postlar arxiv
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