Machine learning books and papers

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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn

📈 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

1 829

Post views

~ 46524 hours

~ 57948 hours

7.45%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

24 522

Repost from Github LLMs

Owen 3 release 📖 Blog @LLM_learning

24 522

📚 The Little Book of Semaphores by Allen B. Downey 📚 Book @Machine_learn

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دوستانی که نتونستند مقالات قبلی رو شرکت کنند این بهترین فرصت....! @Raminmousa

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با عرض سلام می خواهیم مقاله ی جدیدی را تحت عنوان زیر شروع کنیم: Comparative survey on Transfer Learning for multi-modal wound image classification مقالات قبلی که در این رابطه نوشتیم به ترتیب زیر می باشند: تیم 1: [1]چاپ شده در Expert system with application تیم 2:[2] سابمیت شده در Scientific report تیم 3:[3] سابیمت شده در IEEE transaction نفرات 2 تا 5 این مقاله خالی می باشند. این نفرات علاوه بر مرور مقالات و تحلیل نتایج هزینه سرور را نیز متقبل می شوند. [1] Mousa, Ramin, et al. "Multi-modal wound classification using wound image and location by Swin Transformer and Transformer." Expert Systems with Applications (2025): 127077. [2] Mousa, Ramin, et al. "Integrating Vision and Location with Transformers: A Multimodal Deep Learning Framework for Medical Wound Analysis." arXiv preprint arXiv:2504.10452 (2025). [3] Mousa, Ramin, Ehsan Matbooe, and Hakimeh Khojasteh. "Multi-Modal Wound Classification Using Wound Image and Location by Xception and Gaussian Mixture Recurrent Neural Network (GMRNN)." (2025 هزینه نفرات به ترتيب 2:400$ 3:300$ 4:250$ 5:200$ می باشد. ژونال مد نظر Scientific Reprot (Nature) @Raminmousa @Machine_learn @Paper4money

24 522

دوستاني كه در مقالات قبلي نتونستن شركت كنند اين بهترين فرصت....! @Raminmousa

24 522

با عرض سلام می خواهیم مقاله ی جدیدی را تحت عنوان زیر شروع کنیم: Comparative survey on Transfer Learning for multi-modal wound image classification مقالات قبلی که در این رابطه نوشتیم به ترتیب زیر می باشند: تیم 1: [1]چاپ شده در Expert system with application تیم 2:[2] سابمیت شده در Scientific report تیم 3:[3] سابیمت شده در IEEE transaction نفرات 2 تا 5 این مقاله خالی می باشند. این نفرات علاوه بر مرور مقالات و تحلیل نتایج هزینه سرور را نیز متقبل می شوند. [1] Mousa, Ramin, et al. "Multi-modal wound classification using wound image and location by Swin Transformer and Transformer." Expert Systems with Applications (2025): 127077. [2] Mousa, Ramin, et al. "Integrating Vision and Location with Transformers: A Multimodal Deep Learning Framework for Medical Wound Analysis." arXiv preprint arXiv:2504.10452 (2025). [3] Mousa, Ramin, Ehsan Matbooe, and Hakimeh Khojasteh. "Multi-Modal Wound Classification Using Wound Image and Location by Xception and Gaussian Mixture Recurrent Neural Network (GMRNN)." (2025 هزینه نفرات به ترین 2:400$ 3:300$ 4:250$ 5:200$ می باشد. ژونال مد نظر Scientific Reprot (Nature) @Raminmousa @Machine_learn @Paper4money

24 522

A collection of inspiring lists, manuals, cheatsheets, blogs, hacks, one-liners, cli/web tools, and more. 📚 Github @Machine_learn

24 522

ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish 📚 Read @Machine_learn

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https://arxiv.org/abs/2412.00472 Accepted 💪 @Machine_learn

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FREE AI Courses https://www.mltut.com/best-resources-to-learn-artificial-intelligence/ @Machine_learn

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🔥 The Project Gutenberg EBook of First Course in the Theory of Equations, 📚 Book @Machine_learn

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GPT 4.1 Prompting Guide #GPT 📚 Guide @Machine_learn

24 522

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

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Forecasting: Principles and Practice 📚 Book @Machine_learn

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"Handbook of Mathematical Proof" by Edward D. Kim 📚 Link @Machine_learn

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https://t.me/laristarbot?start=_tgr_iCeKtB05OTA0

24 522

A practical guide to building agents by OpenAi 📚 guide @Machine_learn

24 522

Repost from Papers

با عرض سلام می خواهیم مقاله ی مروری جدیدی را تحت عنوان زیر شروع کنیم: Comparative survey on Transfer Learning for multi-modal wound image classification مقالات قبلی که در این رابطه نوشتیم به ترتیب زیر می باشند: تیم 1: [1]چاپ شده در Expert system with application تیم 2:[2] سابمیت شده در Scientific report تیم 3:[3] سابیمت شده در IEEE transaction نفرات 2 تا 5 این مقاله خالی می باشند. این نفرات علاوه بر مرور مقالات و تحلیل نتایج هزینه سرور را نیز متقبل می شوند. [1] Mousa, Ramin, et al. "Multi-modal wound classification using wound image and location by Swin Transformer and Transformer." Expert Systems with Applications (2025): 127077. [2] Mousa, Ramin, et al. "Integrating Vision and Location with Transformers: A Multimodal Deep Learning Framework for Medical Wound Analysis." arXiv preprint arXiv:2504.10452 (2025). [3] Mousa, Ramin, Ehsan Matbooe, and Hakimeh Khojasteh. "Multi-Modal Wound Classification Using Wound Image and Location by Xception and Gaussian Mixture Recurrent Neural Network (GMRNN)." (2025 هزینه نفرات به ترین 2:400$ 3:300$ 4:250$ 5:200$ می باشد. ژونال مد نظر https://link.springer.com/journal/10462 If: 10.7 @Raminmousa @Machine_learn @Paper4money

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Introduction to Graph Neural Networks: A Starting Point for Machine Learning Engineers 📓 Paper @Machine_learn

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Teaching machines the language of biology: Scaling large language models for next-generation single-cell analysis 📚 Blog @Machine_learn