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Machine learning books and papers

Machine learning books and papers

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📈 Analytical overview of Telegram channel Machine learning books and papers

Channel Machine learning books and papers (@machine_learn) in the English language segment is an active participant. Currently, the community unites 24 522 subscribers, ranking 8 070 in the Education category and 13 771 in the Iran region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 522 subscribers.

According to the latest data from 22 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -150 over the last 30 days and by -5 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 7.45%. Within the first 24 hours after publication, content typically collects 1.90% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 1 829 views. Within the first day, a publication typically gains 465 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
  • Thematic interests: Content is focused on key topics such as disorder, psy, مقاله, framework, graph.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn

Thanks to the high frequency of updates (latest data received on 23 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.

24 522
Subscribers
-524 hours
-417 days
-15030 days
Posts Archive
🌟 PyTorch Cheatsheet Cheatsheet @Machine_learn
🌟 PyTorch Cheatsheet Cheatsheet @Machine_learn

تنها دو نفر براي اين مقاله باقي مونده....! @Raminmousa

Datasets Guide 📚 Guide @Machine_learn
Datasets Guide 📚 Guide @Machine_learn

Repost from Papers
با عرض سلام از اين مقاله نفرات ٤ و ٥ باقي مونده دوستاني كه مايل به همكاري هستن لطفا با بنده در ارتباط باشن. یکی از ابزارهای خوبی که بنده تونستم توسعه بدم ابزار Stock Ai می باشد. در این ابزار از ۳۶۰ اندیکاتور استفاده کردم. گزارشات back test این ابزار در ویدیو های زیر موجود می باشد. May 2024 : https://youtu.be/aSS99lynMFQ?si=QSk8VVKhLqO_2Qi3 July 2014: https://youtu.be/ThyZ0mZwsGk?si=FKPK7Hkz-mRx-752&t=209 @Raminmousa

Theory—Theoretical & Mathematical Foundations 📓 Book @Machine_learn
Theory—Theoretical & Mathematical Foundations 📓 Book @Machine_learn

SAE Match 📚 Paper @Machine_learn
SAE Match 📚 Paper @Machine_learn

Mathematics for Machine Learning 📚 Book @Machine_learn
Mathematics for Machine Learning 📚 Book @Machine_learn

الان وقتشه شروع کنی... 🚩 بوتکمپ تخصصی هوش‌مصنوعی 🔘 دوره‌ فشرده‌ آماده‌سازی برای ورود به بازارکار ✨ آموزش تخصصی، کاربردی و ت
الان وقتشه شروع کنی... 🚩 بوتکمپ تخصصی هوش‌مصنوعی 🔘 دوره‌ فشرده‌ آماده‌سازی برای ورود به بازارکار ✨ آموزش تخصصی، کاربردی و تجربه نزدیک به صنعت! ✔️ اساتید مجرب و فعال در حوزه هوش‌مصنوعی ✔️ کار گروهی و شبکه‌سازی‎ ✔️ تمرین و پروژه هدفمند ✔️ منتورینگ اختصاصی ❗️ظرفیت محدود ❗️ فرصت ثبت‌نام فقط تا ۱ اردیبهشت ماه 💳 پرداخت قسطی 🌐 فرم ثبت‌نام: 🔗 https://quera.org/r/7k47n 〰️〰️〰️〰️〰️ #Quera #QBC9

https://arxiv.org/pdf/2504.10452 Integrating Vision and Location with Transformers: A Multimodal Deep Learning Framework for
https://arxiv.org/pdf/2504.10452 Integrating Vision and Location with Transformers: A Multimodal Deep Learning Framework for Medical Wound Analysis New Paper Ramin Mousa Hadis Taherinia Khabiba Abdiyeva Amir Ali Bengari Mohammadmahdi Vahediahmar @Machine_learn

با عرض سلام در ادامه ی کار تحقیقاتی یک مقاله مروری در حوزه پاتولوژی رو شروع کردیم. دوستانی که مایل هستن نفر۲ این موضوع رو می تونن شرکت کنن. Journal: scientific reports https://www.nature.com/srep/ Price: 2: ٢٥ میلیون توضیحات کامل و نحوه نگارش هر بخش رو خودم کمک میکنم. @Raminmousa @Machine_learn @Paper4money

Repost from Github LLMs
SeedLM: Compressing LLM Weights into Seeds of Pseudo-Random Generators 📚 Read @LLM_learning
SeedLM: Compressing LLM Weights into Seeds of Pseudo-Random Generators 📚 Read @LLM_learning

Artificial Intelligence Index Report 2025 📚 Report @Machine_learn
Artificial Intelligence Index Report 2025 📚 Report @Machine_learn

Large Language Model Agent: A Survey on Methodology, Applications and Challenges Paper: https://arxiv.org/pdf/2503.21460v1.pd
Large Language Model Agent: A Survey on Methodology, Applications and Challenges Paper: https://arxiv.org/pdf/2503.21460v1.pdf Code: https://github.com/luo-junyu/awesome-agent-papers @Machine_learn

Compute Forecast 📚 Read @Machine_learn
Compute Forecast 📚 Read @Machine_learn

شنبه شروع اين پروژه مي باشد. دوستاني كه مايل هستند نفر دوم از اين مقاله باقي موند است. @Raminmousa

eswa127077.pdf1.87 MB

eswa127077.pdf1.87 MB

شنبه شروع اين پروژه مي باشد. دوستاني كه مايل هستند نفر دوم از اين مقاله باقي موند است. @Raminmousa

📃 Advances and Mechanisms of RNA–Ligand Interaction Predictions 📎 Study the paper @Machine_learn
📃 Advances and Mechanisms of RNA–Ligand Interaction Predictions 📎 Study the paper @Machine_learn