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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 24 517 subscribers, ranking 8 056 in the Education category and 13 757 in the Iran region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 517 subscribers.

According to the latest data from 24 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -165 over the last 30 days and by -3 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 6.78%. 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 1 663 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 1.
  • 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 25 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.

24 517
Subscribers
-324 hours
-477 days
-16530 days
Posts Archive
🎬 Random Graphs 🎞 Watch ▫️Part 1 ▫️Part 2 @Machine_learn

در پروژه MedicalRec ما نياز به يه نفر جهت مشاركت داريم(جايگاه ٧) Project Title: MedRec: Medical recommender system for image
در پروژه MedicalRec ما نياز به يه نفر جهت مشاركت داريم(جايگاه ٧) Project Title: MedRec: Medical recommender system for image classification without retraining Github: https://github.com/Ramin1Mousa/MedicalRec Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence Impact factor: 20.8 🔸 7- 200$❌ جهت مشارکت می تونید به ایدی بنده پیام بدین. 🧠🧠🧠🧠🧠 @Raminmousa

Foundations of Geometry. DAVID HILBERT, PH. D. 📚 Book @Machine_learn
Foundations of Geometry. DAVID HILBERT, PH. D. 📚 Book @Machine_learn

⭐️ Fast Think-on-Graph: Wider, Deeper and Faster Reasoning of Large Language Model on Knowledge Graph 🖥 Github: https://gith
⭐️ Fast Think-on-Graph: Wider, Deeper and Faster Reasoning of Large Language Model on Knowledge Graph 🖥 Github: https://github.com/dosonleung/fasttog 📕 Paper: https://arxiv.org/abs/2501.14300v1 @Machine_learn

Discrete Matematics and applications 🔗 link @Machine_learn
Discrete Matematics and applications 🔗 link @Machine_learn

در این پروژه امکان اموزش کامل کد نویسی مدل هم برای کسانی که مشارکت میکنن فراهم

Repost from Papers
با عرض سلام پروژه MedicalRec تنها نفر ٤ ام باقي مونده و امشب استارت کار میباشد. 🫥🫥🫥🫥 هدف اصلی این پروژه اموزش یک مدل پیشنهاد دهنده ی مدل برای مسائله طبقه بندی تصاویر پزشکی میباشد که از اموزش مجدد مدل ها جلوگیری میکند. این مسائله با جنبه جلوگیری از مصرف انرژی اموزشی و زمان اموزش مدل ها ارائه می شود. برای این منظور ۵۰۰۰ مقاله در این زمینه جمع اوری شده است. جزئیات بیشتر در لینک گیت قرار دارد. Project Title: MedRec: Medical recommender system for image classification without retraining Github: https://github.com/Ramin1Mousa/MedicalRec Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence Impact factor: 20.8 🔹 2- 600$❌ 🔺 3- 500$❌ 💠 4- 400$✅ 🔺 5- 300$▫️ 🔹 6- 200$❌ 🔸 7- 200$❌ جهت مشارکت می تونید به ایدی بنده پیام بدین. 🧠🧠🧠🧠🧠 @Raminmousa

📄A Survey of Genetic Programming Applications in Modern Biological Research 📎 Study the paper @Machine_learn
📄A Survey of Genetic Programming Applications in Modern Biological Research 📎 Study the paper @Machine_learn

Free access to our secret channels ✅ 📚 Free Data Science Books 👨‍💻 Programming Handwritten Notes 🎁 Python Free Courses 🤖 Learn AI with ChatGPT 🏆 Data Science Projects 👩‍🎓 Coding Projects 💝 Free Coding Certified Courses 💪 Quiz and Job Opportunities And Many More......Join now : https://t.me/machinelearning_deeplearningData Science & AI Jobs Join fast before I delete the link ❤️

📄 Deep Generative Models for Therapeutic Peptide Discovery: A Comprehensive Review 📎 Study the paper @Machine_learn
📄 Deep Generative Models for Therapeutic Peptide Discovery: A Comprehensive Review 📎 Study the paper @Machine_learn

ML, DL, AND AI Cheat Sheet.pdf7.46 MB

Click-Calib: A Robust Extrinsic Calibration Method for Surround-View Systems Surround-View System (SVS) is an essential compo
Click-Calib: A Robust Extrinsic Calibration Method for Surround-View Systems Surround-View System (SVS) is an essential component in Advanced Driver Assistance System (ADAS) and requires precise calibrations. Paper: https://arxiv.org/pdf/2501.01557v2.pdf Code: https://github.com/lwangvaleo/click_calib Dataset: WoodScape @Machine_learn

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/codeprogrammer

Lots of math for CS & ML. Looks pretty interesting. 📚 Book @Machine_learn
Lots of math for CS & ML. Looks pretty interesting. 📚 Book @Machine_learn

Deep Learning 01.pdf31.51 MB

با عرض سلام پروژه MedicalRec تنها نفر ٤ ام باقي مونده هدف اصلی این پروژه اموزش یک مدل پیشنهاد دهنده ی مدل برای مسائله طبقه بندی تصاویر پزشکی میباشد که از اموزش مجدد مدل ها جلوگیری میکند. این مسائله با جنبه جلوگیری از مصرف انرژی اموزشی و زمان اموزش مدل ها ارائه می شود. برای این منظور ۵۰۰۰ مقاله در این زمینه جمع اوری شده است. جزئیات بیشتر در لینک گیت قرار دارد. Project Title: MedRec: Medical recommender system for image classification without retraining Github: https://github.com/Ramin1Mousa/MedicalRec Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence Impact factor: 20.8 🔹 2- 600$❌ 🔺 3- 500$❌ 💠 4- 400$✅ 🔺 5- 300$▫️ 🔹 6- 200$❌ 🔸 7- 200$❌ جهت مشارکت می تونید به ایدی بنده پیام بدین. تنها نفرات ۴ و ۵ باقی مانده....! @Raminmousa

📘 ABI Bioinformatics Guide 🌐 Study @Machine_learn
📘 ABI Bioinformatics Guide 🌐 Study @Machine_learn

Physics IQ Benchmark: Do generative video models learn physical principles from watching videos Book @Machine_learn
Physics IQ Benchmark: Do generative video models learn physical principles from watching videos Book @Machine_learn

📃 Demystifying the black box: A survey on explainable artificial intelligence (XAI) in bioinformatics 📎 Study the paper @Ma
📃 Demystifying the black box: A survey on explainable artificial intelligence (XAI) in bioinformatics 📎 Study the paper @Machine_learn