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Computer Science and Programming

Computer Science and Programming

رفتن به کانال در Telegram

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:

نمایش بیشتر

📈 تحلیل کانال تلگرام Computer Science and Programming

کانال Computer Science and Programming (@machinelearning_programming) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 14 851 مشترک است و جایگاه 8 724 را در دسته فناوری و برنامه‌ها و رتبه 29 599 را در منطقه الهند دارد.

📊 شاخص‌های مخاطب و پویایی

از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 14 851 مشترک جذب کرده است.

بر اساس آخرین داده‌ها در تاریخ 03 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر -150 و در ۲۴ ساعت گذشته برابر -3 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.

  • وضعیت تأیید: تأیید نشده
  • نرخ تعامل (ER): میانگین تعامل مخاطب 14.63% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً N/A% واکنش نسبت به کل مشترکان کسب می‌کند.
  • دسترسی پست‌ها: هر پست به طور میانگین 0 بازدید دریافت می‌کند. در اولین روز معمولاً 0 بازدید جمع‌آوری می‌شود.
  • واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 0 است.
  • علایق موضوعی: محتوا بر موضوعات کلیدی مانند learning, github, engineer, quantization, detection تمرکز دارد.

📝 توضیح و سیاست محتوایی

نویسنده این فضا را محل بیان دیدگاه‌های شخصی توصیف می‌کند:
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:

به لطف به‌روزرسانی‌های پرتکرار (آخرین داده در تاریخ 04 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 فناوری و برنامه‌ها تبدیل کرده‌اند.

14 851
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-227 روز
-15030 روز
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LeGrad: Layerwise Explainability GRADient method for large ViT transformer architectures Explore More: 💻DEMO: you may use de
LeGrad: Layerwise Explainability GRADient method for large ViT transformer architectures Explore More: 💻DEMO: you may use demo 📖Read the Paper: Access Here 💻Source Code: Explore on GitHub Relevance: #AI #machinelearning #deeplearning #computervision join our community: 👉 @MachineLearning_Programming

AiOS: The Future of Human Shape & Pose Recovery Discover AiOS, the cutting-edge, unified framework by SenseTime, HKU, IDEA, S-Lab, and Shanghai AI Lab. AiOS redefines state-of-the-art expressive pose and shape recovery, seamlessly integrating advanced features without the need for separate human detection steps. Highlights: ✅First-of-its-Kind: Single-stage EHPS with zero extra detection networks. ✅Innovative Design: Unique "Human-as-Tokens" concept for deeper insights. ✅Enhanced Dynamics: Sophisticated attention to human relationships. ✅Comprehensive Analysis: Unified feature system for unparalleled whole-body understanding. ✅Unmatched Performance: Top-tier results sans ground truth bounding boxes. Explore More: Project Page Read the Paper

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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_rRW2scgfRhOTc0https://t.me/DataScienceM

This channels is for Programmers, Coders, Software Engineers. 0- Python 1- Data Science 2- Machine Learning 3- Data Visualiza
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🌟 Discover 6DRepNet: The Ultimate Head Pose Estimation Model! Features: * State-of-the-art accuracy * Comprehensive tools for training, testing, and inference * Easy setup with Conda * Supports multiple datasets Watch the performance showcase on GitHub for future advancements. [Source Code] [Paper] join our community: 👉 @MachineLearning_Programming

🌴🌴Direct-a-Video: driving Video Generation🌴🌴 👉Direct-a-Video is a text-to-video generation framework that allows users to individually or jointly control the camera movement and/or object motion. Authors: City University of HK, Kuaishou Tech & Tianjin. 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Decoupling camera/object motion in gen-AI ✅Allowing users to independently/jointly control ✅Novel temporal cross-attention for cam motion ✅Training-free spatial cross-attention for objects ✅Driving object generation via bounding boxes hashtag#artificialintelligence hashtag#machinelearning hashtag#ml hashtag#AI hashtag#deeplearning hashtag#computervision hashtag#AIwithPapers hashtag#metaverse 👉Channel: @MachineLearning_Programming 👉Paper https://arxiv.org/pdf/2402.03162.pdf 👉Project https://direct-a-video.github.io/

EfficientViT - SAM:69x Faster SAM: Multi-Scale Linear Attention for High-Resolution Dense Prediction 1. Channel: @deeplearnin
EfficientViT - SAM:69x Faster SAM: Multi-Scale Linear Attention for High-Resolution Dense Prediction 1. Channel: @deeplearning_ai 2.Source Code: https://github.com/mit-han-lab/efficientvit 3. Paper: https://arxiv.org/abs/2402.05008

This channels is for Programmers, Coders, Software Engineers. 0- Python 1- Data Science 2- Machine Learning 3- Data Visualiza
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