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Github Top Repositories

Github Top Repositories

رفتن به کانال در Telegram

Top GitHub repositories in one place 🚀 Explore the best projects in programming, AI, data science, and more.

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📈 تحلیل کانال تلگرام Github Top Repositories

کانال Github Top Repositories (@githubre) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 13 354 مشترک است و جایگاه 15 230 را در دسته آموزش و رتبه 31 848 را در منطقه الهند دارد.

📊 شاخص‌های مخاطب و پویایی

از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 13 354 مشترک جذب کرده است.

بر اساس آخرین داده‌ها در تاریخ 18 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 431 و در ۲۴ ساعت گذشته برابر 13 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.

  • وضعیت تأیید: تأیید نشده
  • نرخ تعامل (ER): میانگین تعامل مخاطب 1.06% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 0.78% واکنش نسبت به کل مشترکان کسب می‌کند.
  • دسترسی پست‌ها: هر پست به طور میانگین 142 بازدید دریافت می‌کند. در اولین روز معمولاً 104 بازدید جمع‌آوری می‌شود.
  • واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 1 است.
  • علایق موضوعی: محتوا بر موضوعات کلیدی مانند repository, fork, programming, statistic, description تمرکز دارد.

📝 توضیح و سیاست محتوایی

نویسنده این فضا را محل بیان دیدگاه‌های شخصی توصیف می‌کند:
Top GitHub repositories in one place 🚀 Explore the best projects in programming, AI, data science, and more.

به لطف به‌روزرسانی‌های پرتکرار (آخرین داده در تاریخ 19 ژوئن, 2026)، کانال همواره به‌روز و دارای دسترسی بالاست. تحلیل‌ها نشان می‌دهد مخاطبان به‌طور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته آموزش تبدیل کرده‌اند.

13 354
مشترکین
+1324 ساعت
+757 روز
+43130 روز
آرشیو پست ها
😍😍😍😍😍😍😍😍 It’s truly fascinating — definitely worth diving deeper into and working on! 😍😍😍😍😍😍😍😍

🔖 ImageBind: One Embedding Space To Bind Them All 📝 This project is a significant step forward in understanding and connect
🔖 ImageBind: One Embedding Space To Bind Them All 📝 This project is a significant step forward in understanding and connecting information from diverse sources like images, text, audio, video, and even motion sensor data. ⚙️ Supports 6 Modalities: 📷 Image 📝 Text 🔈 Audi 🎥 Video 🦴 IMU sensor data (e.g., accelerometer) 🙄 Depth/Thermal & 3D data Interestingly, only some modalities had labels, yet ImageBind learned to align them through self-supervised learning. 💫 Key Features: ..No need for paired data (e.g., images and audio don’t have to be aligned)..Leverages contrastive learning for learning joint embedding space ..Competes with CLIP and AudioCLIP, but with better accuracy and coverage..Enables zero-shot retrieval (e.g., finding relevant video using just a sentence) 📌 Repo: https://github.com/facebookresearch/ImageBind 🔍 By: https://t.me/DataScienceN 🌟 #ImageBind #MultimodalAI #MetaAI #DeepLearning #SelfSupervised

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🔖 Project name : Analysis of Covid-19 chest x-rays 📝 In order to diagnose patients with Covid-19, the analysis of chest X-r
🔖 Project name : Analysis of Covid-19 chest x-rays 📝 In order to diagnose patients with Covid-19, the analysis of chest X-rays is a possibility to be explored to more easily detect positive cases. If the classification through deep learning of such data proves effective in detecting positive cases, then this method can be used in hospitals and clinics when traditional testing cannot be done. 📌 Repo: https://github.com/rehabaam/ds_covid19_project 🔍 By: https://t.me/DataScienceN 🌟

📌 Project name: Cat/Dog/Fox Lightning 2024 By: https://t.me/DataScienceN 🌟

📌 Project name: Cat/Dog/Fox Lightning 2024 📝 Language: #Python 🔗 Dataset Link: https://www.kaggle.com/datasets/snmahsa/ani
📌 Project name: Cat/Dog/Fox Lightning 2024 📝 Language: #Python 🔗 Dataset Link: https://www.kaggle.com/datasets/snmahsa/animal-image-dataset-cats-dogs-and-foxes 🖋 Download the dataset offline: https://t.me/datasets1/668

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