Machine learning books and papers
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Admin: @Raminmousa1 ID: @Machine_learn link: https://t.me/Machine_learn
Show more📈 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 24 239 subscribers, ranking 8 055 in the Education category and 14 129 in the Iran region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 239 subscribers.
According to the latest data from 05 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -185 over the last 30 days and by -14 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 6.25%. Within the first 24 hours after publication, content typically collects 1.97% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 516 views. Within the first day, a publication typically gains 478 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: @Raminmousa1
ID: @Machine_learn
link: https://t.me/Machine_learn”
Thanks to the high frequency of updates (latest data received on 06 October, 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.
24 239
Subscribers
-1424 hours
-417 days
-18530 days
Posts Archive
High-Impact Research Paper – MedicalRec / GROKRec
Title: MedicalRec
Price: $1,000 USD
Looking for a ready-to-publish, novel research paper in medical AI and sustainable deep learning?
This paper introduces GROKRec, an innovative recommender framework that uses embedding vectors from the Grok language model combined with numerical features to recommend the best deep learning model for any medical image classification task — without the need to train dozens of models on the target dataset.
Key Highlights:
Addresses major real-world problems: high computational cost, energy consumption, carbon emissions, and e-waste caused by training large DL models.
Built on a newly curated public dataset MedicalRec-Bench II containing 3,500 research papers and over 6,000 model evaluation records across diverse medical imaging tasks.
Evaluated under four feature configurations (MedicalRec I) using 13 different models.
Achieves strong performance with HitRate@100 ranging from 72.43% to 77.08% — the highest among compared approaches.
Uses composite loss functions and regularization techniques for accurate recommendations.
Fully eliminates the trial-and-error process of training multiple models, significantly reducing carbon footprint.
This is a complete, self-contained research contribution with a novel dataset and a practical, environmentally conscious solution for the medical AI community.
Ideal for: Researchers, academic publishers, journals, or institutions looking for high-quality, ready-to-use work in medical image analysis, recommender systems, and green AI.
Price: $1,000 USD (one-time transfer of ownership/rights as agreed).
Interested? Contact me for the full manuscript, dataset details, or to discuss terms.
@Raminmousa1
🔥 FrameMorrow: Future-guided Frame Selection with Prospective Tokens for Long-Horizon Video Generation
🔗 Links:
GitHub: https://github.com/huggingface
arXiv: https://arxiv.org/abs/2609.38839
PDF: https://arxiv.org/pdf/2609.38839
Project Page: https://yinbo0927.github.io/FrameMorrow/
@Machine_learn
🔥 TAPNext: Tracking Any Point (TAP) as Next Token Prediction
🔗 Links:
GitHub: https://github.com/huggingface
arXiv: https://arxiv.org/abs/2504.05579
PDF: https://arxiv.org/pdf/2504.05579
Project Page: https://tap-next.github.io/
@machine_learn
Repost from ابر ویراک
☁️ ابرک ویراک را 24 ساعت رایگان تست کنید!
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بدون نیاز به تعهد بلندمدت، ابرک خودتان را بسازید، عملکرد سرویس را بررسی کنید و بعد تصمیم بگیرید.
برای دریافت کد تست رایگان، کلمه «تست» را به آیدی زیر ارسال کنید:
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☎️ 02191555530
🌐 virakcloud.com
📖 "A Little Book on the Fundamentals of Generative AI" - an intuitive introduction to the mathematics:
arxiv.org/pdf/2605.29713
#GenerativeAI #Mathematics #DeepLearning #AIResearch #MachineLearning #arXiv
✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk
@Machine_learn
🔎 #وبینار عملی ریشه‌یابی خطا با Sentry
🔮 در وبینار «از گزارش خطای کاربر تا ریشه‌یابی مشکل با Sentry» با یک سناریوی عملی، مسیر رسیدن از گزارش یک مشکل به سرنخ‌های لازم برای ریشه‌یابی آن را دنبال می‌کنیم.
در این وبینار:
• #سنتری را راه‌اندازی می‌کنیم و به یک اپلیکیشن متصل می‌شویم
• یک خطا را ثبت می‌کنیم و با اطلاعاتی که Sentry در اختیارمان می‌گذارد، علت آن را بررسی می‌کنیم
• با Session Replay اتفاقات پیش از بروز خطا را از دید کاربر بازبینی می‌کنیم
• با Trace و Span مسیر اجرای عملیات را دنبال و گلوگاه‌های عملکردی اپلیکیشن را پیدا می‌کنیم.
📣 ارائه‌دهنده: علیرضا بانشی | SaaS Team Lead در هم‌روش
📅 سه‌شنبه ۲۱ مهر ساعت ۱۹
🔗 ثبت‌نام رایگان:
https://hmrv.sh/EdN7gd
☁️@hamravesh
با عرض سلام این مقاله به صورت کامل واگذار میشه به همراه پیاده سازی مجموعه داده و قالب latex. هزینه کار ۱۰۰۰ دلار
@Raminmousa1
Understanding Attention
From Q, K, V to Modern Transformer Attention
https://drive.google.com/file/d/1fCHQ5xCQJ6jZszAYf-qP3VIySbzFIEDv/view
@Machine_learn
Uniface
Automate face detection, recognition, and analysis of key facial landmarks with the Uniface Python library.
https://github.com/yakhyo/uniface
@Machine_learn
For Sale: High-Impact Research Paper – MedicalRec / GROKRec
Title: MedicalRec
Price: $1,000 USD
Looking for a ready-to-publish, novel research paper in medical AI and sustainable deep learning?
This paper introduces GROKRec, an innovative recommender framework that uses embedding vectors from the Grok language model combined with numerical features to recommend the best deep learning model for any medical image classification task — without the need to train dozens of models on the target dataset.
Key Highlights:
Addresses major real-world problems: high computational cost, energy consumption, carbon emissions, and e-waste caused by training large DL models.
Built on a newly curated public dataset MedicalRec-Bench II containing 3,500 research papers and over 6,000 model evaluation records across diverse medical imaging tasks.
Evaluated under four feature configurations (MedicalRec I) using 13 different models.
Achieves strong performance with HitRate@100 ranging from 72.43% to 77.08% — the highest among compared approaches.
Uses composite loss functions and regularization techniques for accurate recommendations.
Fully eliminates the trial-and-error process of training multiple models, significantly reducing carbon footprint.
This is a complete, self-contained research contribution with a novel dataset and a practical, environmentally conscious solution for the medical AI community.
Ideal for: Researchers, academic publishers, journals, or institutions looking for high-quality, ready-to-use work in medical image analysis, recommender systems, and green AI.
Price: $1,000 USD (one-time transfer of ownership/rights as agreed).
Interested? Contact me for the full manuscript, dataset details, or to discuss terms.
@Raminmousa1
For Sale: High-Impact Research Paper – MedicalRec / GROKRec
Title: MedicalRec
Price: $1,000 USD
Looking for a ready-to-publish, novel research paper in medical AI and sustainable deep learning?
This paper introduces GROKRec, an innovative recommender framework that uses embedding vectors from the Grok language model combined with numerical features to recommend the best deep learning model for any medical image classification task — without the need to train dozens of models on the target dataset.
Key Highlights:
Addresses major real-world problems: high computational cost, energy consumption, carbon emissions, and e-waste caused by training large DL models.
Built on a newly curated public dataset MedicalRec-Bench II containing 3,500 research papers and over 6,000 model evaluation records across diverse medical imaging tasks.
Evaluated under four feature configurations (MedicalRec I) using 13 different models.
Achieves strong performance with HitRate@100 ranging from 72.43% to 77.08% — the highest among compared approaches.
Uses composite loss functions and regularization techniques for accurate recommendations.
Fully eliminates the trial-and-error process of training multiple models, significantly reducing carbon footprint.
This is a complete, self-contained research contribution with a novel dataset and a practical, environmentally conscious solution for the medical AI community.
Ideal for: Researchers, academic publishers, journals, or institutions looking for high-quality, ready-to-use work in medical image analysis, recommender systems, and green AI.
Price: $1,000 USD (one-time transfer of ownership/rights as agreed).
Interested? Contact me for the full manuscript, dataset details, or to discuss terms.
@Raminmousa1
❇️ MindDrive: A Vision-Language-Action Model for Autonomous Driving via Online Reinforcement Learning 🔥
Source code: https://github.com/xiaomi-mlab/Minddrive
@Machine_learn
ReCamMaster: Camera-Controlled Generative Rendering from A Single Video
Source code: https://github.com/KlingAIResearch/ReCamMaster
@Machine_learn
با عرض سلام یک هفته تا سابمیت این مقاله وقت مونده. دوستانی که نیاز دارن میتونن مشارکت کنند.
@Raminmousa1
This repository contains Jupyter notebooks for the O'Reilly book "Transformers: The Definitive Guide."
It includes code for computer vision tasks, time series analysis, audio processing, and reinforcement learning.
https://github.com/Nicolepcx/transformers-the-definitive-guide
@Machine_learn
با عرض سلام برای یکی از مقالاتمون تحت عنون زیر نیازمند نفر دوم و سوم هستیم.
Price: 2 --> 200$
Price 3--> 150$
Title:Skin cancer diagnosis (scd) using efficientnet-wavelet and Optimization algortithms
@Raminmousa1
🔖Computer Science Fundamentals from MIT
We found the textbook Mathematics for Computer Science – covering the mathematics that underlies algorithms and computer science.
Logic, graphs, combinatorics, probability, induction, recurrence relations, and discrete structures – all in one place.
⛓️ Link to the textbook
https://ocw.mit.edu/courses/6-042j-mathematics-for-computer-science-spring-2015/mit6_042js15_textbook.pdf
@Machine_learn
Repost from ابر ویراک
☁️ 24 ساعت تست رایگان سرور ابری ویراک، حالا با اعتبار هدیه بیشتر!
اگر قصد راه‌اندازی یا تمدید سرویس‌های ابری خود را دارید، الان بهترین زمان است.
✨ تا پایان مردادماه:
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🎁 10% شارژ بیشتر هدیه روی تمام واریزی‌های بعدی
🎁 24 ساعت تست رایگان
فرقی نمی‌کند اولین بار است که ویراک را انتخاب می‌کنید یا از قبل همراه ما بوده‌اید؛ با هر شارژ، اعتبار بیشتری دریافت می‌کنید و همان زیرساخت قدرتمند را با هزینه کمتر در اختیار خواهید داشت.
برای دریافت کد تست رایگان کلمه *«تست»* رو به آیدی زیر ارسال کنید.
https://t.me/cloud_virak
👇 پس از دریافت کد تست رایگان وارد پنل VirakCloud شوید ، کد تخفیف خود را وارد نمایید و ابرک خود را بسازید:
🔗 https://B2n.ir/qy4432
☎️ 02191555530
