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Computer Science and Programming

Computer Science and Programming

الذهاب إلى القناة على Telegram

Channel specialized for advanced topics of: * Artificial intelligence, * Machine Learning, * Deep Learning, * Computer Vision, * Data Science * Python Admin: @otchebuch Memes: @memes_programming Ads: @Source_Ads, https://telega.io/c/computer_science

إظهار المزيد

📈 نظرة تحليلية على قناة تيليجرام Computer Science and Programming

تُعد قناة Computer Science and Programming (@computer_science_and_programming) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 142 737 مشتركاً، محتلاً المرتبة 816 في فئة التكنولوجيات والتطبيقات والمرتبة 87 في منطقة إيطاليا.

📊 مؤشرات الجمهور والحراك

منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 142 737 مشتركاً.

بحسب آخر البيانات بتاريخ 14 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار -1 292، وفي آخر 24 ساعة بمقدار -44، مع بقاء الوصول العام مرتفعاً.

  • حالة التحقق: غير موثّقة
  • معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 6.29‎%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 1.82‎% من ردود الفعل نسبةً إلى إجمالي المشتركين.
  • وصول المنشورات: يحصل كل منشور على متوسط 8 976 مشاهدة. وخلال اليوم الأول يجمع عادةً 2 595 مشاهدة.
  • التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 17.
  • الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل sellerflash, github, developer, pricing, waybienad.

📝 الوصف وسياسة المحتوى

يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
Channel specialized for advanced topics of: * Artificial intelligence, * Machine Learning, * Deep Learning, * Computer Vision, * Data Science * Python Admin: @otchebuch Memes: @memes_programming Ads: @Source_Ads, https://telega.io/c/computer_sc...

بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 15 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.

142 737
المشتركون
-4424 ساعات
-2007 أيام
-1 29230 أيام
أرشيف المشاركات
YOLACT (You Only Look At CoefficienTs) - Real-time Instance Segmentation Results are impressive, above 30 FPS on COCO test-de
YOLACT (You Only Look At CoefficienTs) - Real-time Instance Segmentation Results are impressive, above 30 FPS on COCO test-dev

However, great resource from data-flair team and there are waiting you 240+ Python Tutorials from scratch (under advanced, intermediate, beginner categories): https://data-flair.training/blogs/python-tutorials-home/ and you'll also follow their telegram channels for fresh news from original source: https://t.me/dataflair

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Happy new year. I would like to share channel's progress for 2019 and we have +29 874 new members for this year. Thank you fo
Happy new year. I would like to share channel's progress for 2019 and we have +29 874 new members for this year. Thank you for all members of channel.

⚠ Message was hidden by channel owner

Due to your interest and some specific points, you will read about in more detail from the report: https://hai.stanford.edu/sites/g/files/sbiybj10986/f/ai_index_2019_report.pdf?fbclid=IwAR228NxD7QCdksNYkSPZ2vcpm5Jzk5zCGx9v0NpsAkQVOspv85MvG3LK3wE

2019 is also finishing with great achievements in AI field. Thanks to the extended report from 'artificial intelligence index
2019 is also finishing with great achievements in AI field. Thanks to the extended report from 'artificial intelligence index' which I highlighted more specific ones (Of cource is just my choise only): 👉 AI Research went crazy. Between 1998 and 2018, there’s been a 300% increase in the publication of peer-reviewed papers on AI. 👉 Attendance at conferences went crazy too, for eg. NeurIPS, got some 13,500 attendees this year, up 800% from 2012. 👉 Education too bumped up, a lot of folks took up MSc / PhD with something in Machine Learning 👉 USA still leads in AI, no matter what other countries say 👉 AI algorithms are becoming cheaper and mainstream 👉 self driving vehicles market is coming of age and raking in a lot of investments

I think, every AI lovers are waiting for AI debate: Yoshua Bengio and Gary Marcus, which a decade that has revived the field
I think, every AI lovers are waiting for AI debate: Yoshua Bengio and Gary Marcus, which a decade that has revived the field of AI

BMW shares AI algorithms used in production, available on GitHub
BMW shares AI algorithms used in production, available on GitHub

Things you need to consider about your algorithm is working or not

Free AI Resources Find The Most Updated and Free Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Mathematics, Python Programming Resources. (Last Update: December 4, 2019)

RoboNet Project page: https://www.robonet.wiki/

RoboNet: A Dataset for Large-Scale Multi-Robot Learning (15 million video frames, 7 Robot platform).
RoboNet: A Dataset for Large-Scale Multi-Robot Learning (15 million video frames, 7 Robot platform).