متاح الآن! بحث تيليغرام 2025 — أهم رؤى العام 
Artificial Intelligence && Deep Learning
الذهاب إلى القناة على Telegram
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:
إظهار المزيد📈 نظرة تحليلية على قناة تيليجرام Artificial Intelligence && Deep Learning
تُعد قناة Artificial Intelligence && Deep Learning (@deeplearning_ai) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 58 023 مشتركاً، محتلاً المرتبة 2 289 في فئة التكنولوجيات والتطبيقات والمرتبة 6 003 في منطقة الهند.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 58 023 مشتركاً.
بحسب آخر البيانات بتاريخ 24 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار -193، وفي آخر 24 ساعة بمقدار 17، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 9.42%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً N/A% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 5 467 مشاهدة. وخلال اليوم الأول يجمع عادةً 0 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 16.
- الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل github, learning, estimation, dataset, engineer.
📝 الوصف وسياسة المحتوى
يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“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:”
بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 25 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.
58 023
المشتركون
+1724 ساعات
-237 أيام
-19330 أيام
أرشيف المشاركات
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 🌹🌹
@deeplearning_ai
+1#—————CVPR_2021—————
RefineMask: Towards High-Quality Instance Segmentation
with Fine-Grained Features (CVPR 2021)
[paper] : download paper and enjoy
source: use source code and get awesome result
invite your friends and get latest news and sources on AI
RefineMask: Towards High-Quality Instance Segmentation
with Fine-Grained Features (CVPR 2021)
[paper] : download paper and enjoy
source: use source code and get awesome result
invite your friends and get latest news and sources on AI
—————— 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 🌹🌹
@deeplearning_ai
Papers with Code 2021 : A Year in Review.
Papers with Code indexes various machine learning artifacts — papers, code, results — to facilitate discovery and comparison. Using this data we can get a sense of what the ML community found useful and interesting this year. Below we summarize the top trending papers, libraries and datasets for 2021 on Papers with Code.
https://medium.com/paperswithcode/papers-with-code-2021-a-year-in-review-de75d5a77b8b
👉👉@deeplearning_ai
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
NeurIPS 2021—10 papers you shouldn’t miss
2334 papers, 60 workshops, 8 keynote speakers, 15k+ attendees. A dense landscape that’s hard to navigate without a good guide and map, so here are some of our ideas!
https://towardsdatascience.com/neurips-2021-10-papers-you-shouldnt-miss-80f9c0793a3a
invite your friends 🌹🌹
@deeplearning_ai
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/.
Review — DeepFace: Closing the Gap to Human-Level Performance in Face Verification
DeepFace for Face Verification After Face Alignment
https://sh-tsang.medium.com/review-deepface-closing-the-gap-to-human-level-performance-in-face-verification-973442ad7850
https://t.me/DeepLearning_ai
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
Deep Learning with Python (2021)
Invite your friends 🌹🌹
@deeplearning_ai
Deep Learning with Python (2021)
Invite your friends 🌹🌹
@deeplearning_ai
Summary
Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples.
What's Inside:
* Deep learning from first principles
* Setting up your own deep-learning environment
* Image-classification models
* Deep learning for text and sequences
* Neural style transfer, text generation, and image generation
@Deeplearning_aiDeep Learning with Python (2021)
Invite your friends 🌹🌹
@deeplearning_ai
Summary
Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples.
What's Inside:
* Deep learning from first principles
* Setting up your own deep-learning environment
* Image-classification models
* Deep learning for text and sequences
* Neural style transfer, text generation, and image generation
@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
