متاح الآن! بحث تيليغرام 2025 — أهم رؤى العام 
Github Top Repositories
الذهاب إلى القناة على Telegram
Top GitHub repositories in one place 🚀 Explore the best projects in programming, AI, data science, and more.
إظهار المزيد📈 نظرة تحليلية على قناة تيليجرام Github Top Repositories
تُعد قناة Github Top Repositories (@githubre) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 13 353 مشتركاً، محتلاً المرتبة 15 253 في فئة التعليم والمرتبة 31 961 في منطقة الهند.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 13 353 مشتركاً.
بحسب آخر البيانات بتاريخ 17 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 426، وفي آخر 24 ساعة بمقدار 9، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 1.07%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 0.79% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 143 مشاهدة. وخلال اليوم الأول يجمع عادةً 106 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 1.
- الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل repository, fork, programming, statistic, description.
📝 الوصف وسياسة المحتوى
يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“Top GitHub repositories in one place 🚀
Explore the best projects in programming, AI, data science, and more.”
بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 18 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التعليم.
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المشتركون
+924 ساعات
+677 أيام
+42630 أيام
أرشيف المشاركات
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Imagine an AI assistant for data scientists that builds an entire Jupyter notebook on request
Data Copilot - is like Cursor, but it is designed for working with data📊
Imports Dataset, cleans it, teaches models, tests it - all in one pass✅.
And yes, he is completely open-source 🤝.
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Some projects don’t scream for attention — they quietly change the way we learn🧑🎓.
Python Data Science Handbook is one of them📖.
🧑🎓 What is it?
A powerful open-source GitHub repository by Jake VanderPlas. It turns his bestselling book into interactive Jupyter notebooks that teach: 🧮 NumPy → fast array operations 📊 Pandas → data cleaning & analysis 📈 Matplotlib → clear visualizations 🤖 Scikit-learn → machine learning made simple 🛸 IPython → efficient coding environment🎚️Run everything instantly via Binder or Google Colab — no setup needed⚙.
Don’t chase complexity... Start with clarity.🖥 GitHub .
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Repost from Machine Learning with Python
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Repost from Machine Learning with Python
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🚀 Master Python with Ease!
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Whether you're brushing up on the basics or diving into data science, these sheets will save you time and boost your productivity.
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💃 GENMO: Generalist Human Motion by NVIDIA 💃
NVIDIA introduces GENMO, a unified generalist model for human motion that seamlessly combines motion estimation and generation within a single framework. GENMO supports conditioning on videos, 2D keypoints, text, music, and 3D keyframes, enabling highly versatile motion understanding and synthesis.
Currently, no official code release is available.
Review:
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Paper:
https://lnkd.in/ds36BY49
Project Page:
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#NVIDIA #GENMO #HumanMotion #DeepLearning #AI #ComputerVision #MotionGeneration #MachineLearning #MultimodalAI #3DReconstruction✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk 📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
13 353
Repost from Machine Learning with Python
🎯 ابدأ رحلتك الاحترافية في البرمجة مع
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هل ترغب بتعلم لغة البرمجة الأكثر طلبًا في العالم؟
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🔥Powerful Combo: Ultralytics YOLO11 + Sony Semicon | AITRIOS (Global) Platform + Raspberry Pi
We’ve recently updated our Sony IMX model export to fully support YOLO11n detection models! This means you can now seamlessly run YOLO11n models directly on Raspberry Pi AI Cameras powered by the Sony IMX500 sensor — making it even easier to develop advanced Edge AI applications. 💡
To test this new export workflow, I trained a model on the VisDrone dataset and exported it using the following command:
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yolo export model=<path_to_drone_model> format=imx data=VisDrone.yaml
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Sure! Here's the English version of your post, translated naturally and professionally:
---
🔥 Powerful Combo: Ultralytics YOLO11 + Sony Semicon | AITRIOS (Global) Platform + Raspberry Pi
We’ve recently updated our Sony IMX model export to fully support YOLO11n detection models! This means you can now seamlessly run YOLO11n models directly on Raspberry Pi AI Cameras powered by the Sony IMX500 sensor — making it even easier to develop advanced Edge AI applications. 💡
To test this new export workflow, I trained a model on the VisDrone dataset and exported it using the following command:
👉
yolo export model=<path_to_drone_model> format=imx data=VisDrone.yaml13 353
Repost from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://t.me/addlist/8_rRW2scgfRhOTc0
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13 353
Follow me on LinkedIn for more projects and jobs
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13 353
Repost from Machine Learning with Python
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13 353
Repost from Python Courses & Resources
🎁 Your balance is credited $4,000 , the owner of the channel wants to contact you!
Dear subscriber, we would like to thank you very much for supporting our channel, and as a token of our gratitude we would like to provide you with free access to Lisa's investor channel, with the help of which you can earn today
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🚀 The new HQ-SAM (High-Quality Segment Anything Model) has just been added to the Hugging Face Transformers library!
This is an enhanced version of the original SAM (Segment Anything Model) introduced by Meta in 2023. HQ-SAM significantly improves the segmentation of fine and detailed objects, while preserving all the powerful features of SAM — including prompt-based interaction, fast inference, and strong zero-shot performance. That means you can easily switch to HQ-SAM wherever you used SAM!
The improvements come from just a few additional learnable parameters. The authors collected a high-quality dataset with 44,000 fine-grained masks from various sources, and impressively trained the model in just 4 hours using 8 GPUs — all while keeping the core SAM weights frozen.
The newly introduced parameters include:
* A High-Quality Token
* A Global-Local Feature Fusion mechanism
This work was presented at NeurIPS 2023 and still holds state-of-the-art performance in zero-shot segmentation on the SGinW benchmark.
📄 Documentation: https://lnkd.in/e5iDT6Tf
🧠 Model Access: https://lnkd.in/ehS6ZUyv
💻 Source Code: https://lnkd.in/eg5qiKC2
#ArtificialIntelligence #ComputerVision #Transformers #Segmentation #DeepLearning #PretrainedModels #ResearchAndDevelopment #AdvancedModels #ImageAnalysis #HQ_SAM #SegmentAnything #SAMmodel #ZeroShotSegmentation #NeurIPS2023 #AIresearch #FoundationModels #OpenSourceAI #SOTA
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