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AI and Machine Learning

AI and Machine Learning

رفتن به کانال در Telegram

Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses

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📈 تحلیل کانال تلگرام AI and Machine Learning

کانال AI and Machine Learning (@machine_learning_courses) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 94 085 مشترک است و جایگاه 1 556 را در دسته آموزش و رتبه 3 013 را در منطقه الهند دارد.

📊 شاخص‌های مخاطب و پویایی

از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 94 085 مشترک جذب کرده است.

بر اساس آخرین داده‌ها در تاریخ 25 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 981 و در ۲۴ ساعت گذشته برابر 47 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.

  • وضعیت تأیید: تأیید نشده
  • نرخ تعامل (ER): میانگین تعامل مخاطب 6.77% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 2.34% واکنش نسبت به کل مشترکان کسب می‌کند.
  • دسترسی پست‌ها: هر پست به طور میانگین 6 370 بازدید دریافت می‌کند. در اولین روز معمولاً 2 203 بازدید جمع‌آوری می‌شود.
  • واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 9 است.
  • علایق موضوعی: محتوا بر موضوعات کلیدی مانند learning, llm, linkedin, linux, udemy تمرکز دارد.

📝 توضیح و سیاست محتوایی

نویسنده این فضا را محل بیان دیدگاه‌های شخصی توصیف می‌کند:
Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses

به لطف به‌روزرسانی‌های پرتکرار (آخرین داده در تاریخ 26 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.

94 085
مشترکین
+4724 ساعت
+1877 روز
+98130 روز
آرشیو پست ها
08 - A3C Implementation - Part 04

08 - A3C Implementation - Part 03

08 - A3C Implementation - Part 02

08 - A3C Implementation - Part 01

07 - A3C Intuition

06 - Deep Convolutional QLearning Implementation - Part 02

06 - Deep Convolutional QLearning Implementation - Part 01

05 - Deep Convolutional QLearning Intuition

04 - Deep QLearning Implementation - Part 03

04 - Deep QLearning Implementation - Part 02

04 - Deep QLearning Implementation - Part 01

03 - Deep QLearning Intuition

02 - QLearning Intuition

01 - Welcome to the course

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120+ Tutorials more than 65+ hours – From beginner to advanced AI concepts. Hands-On Coding – Real projects with step-by-step
 120+ Tutorials more than 65+ hours – From beginner to advanced AI concepts. Hands-On Coding – Real projects with step-by-step guidance. Advanced Topics – RAG, agents, multi-agent systems, and more. Regular Updates – Stay ahead with fresh, evolving content. Subscribe Now – Master AI and build cutting-edge applications! 🚀 Don’t miss this chance to tap into the full potential of Generative AI. Subscribe Now Here and join a growing community of professionals and enthusiasts ready to shape the future of technology—starting with these tutorials. Whether you’re chasing a career in AI or just love exploring innovations, this playlist is the key to unlocking tomorrow’s possibilities today. PLAYLIST: Link GITHUB: Link

Understanding Generative AI: It's Not AGI What is Generative AI? Generative AI refers to algorithms designed to generate new
Understanding Generative AI: It's Not AGI What is Generative AI? Generative AI refers to algorithms designed to generate new content — from text to images — based on patterns learned from a dataset. Technologies like GPT-4 and DALL-E are popular examples, extensively used for tasks ranging from writing articles to designing graphics. How Does Generative AI Work? 1 Training: Generative AI models are trained on large datasets, learning the structure, style, and intricacies of the data without human intervention. 2 Pattern Recognition: Through training, these models recognize patterns and correlations in the data, enabling them to predict and generate similar outputs. 3 Output Generation: When provided with a prompt, generative AI uses its training to produce content that aligns with what it has learned, attempting to mimic the input style or respond to the query coherently. Generative AI vs. AGI: • Specialization: Generative AI excels in specific tasks it's trained for but lacks the ability to perform beyond its training. • No Consciousness or Understanding: Unlike AGI, generative AI does not possess consciousness, understanding, or reasoning. It doesn't "think" like humans; it merely processes data based on pre-defined mathematical and probabilistic models. • Task-Specific: Generative AI operates within the confines of its programming and training, contrasting with AGI's potential to perform any intellectual task that a human can. Why It Matters: Understanding the capabilities and limitations of generative AI helps set realistic expectations for its applications. It's a powerful tool for specific tasks but is far from the sci-fi notion of an all-knowing, all-purpose AI. Generative AI is nowhere near AGI, it even works on different principles. It basically is an average function for non-numerical data. It can create an average text or an average picture from all the texts and pictures it has seen.

Machine Learning Roadmap 👆
Machine Learning Roadmap 👆

Open Source LLMs Part-1
Open Source LLMs Part-1