Machine learning books and papers
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Admin: @Raminmousa1 ID: @Machine_learn link: https://t.me/Machine_learn
Mostrar más📈 Análisis del canal de Telegram Machine learning books and papers
El canal Machine learning books and papers (@machine_learn) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 24 121 suscriptores, ocupando la posición 8 279 en la categoría Educación y el puesto 14 626 en la región Irán.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 24 121 suscriptores.
Según los últimos datos del 26 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -170, y en las últimas 24 horas de -3, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 7.29%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.93% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 758 visualizaciones. En el primer día suele acumular 466 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 2.
- Intereses temáticos: El contenido se centra en temas clave como disorder, psy, مقاله, framework, graph.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Admin: @Raminmousa1
ID: @Machine_learn
link: https://t.me/Machine_learn”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 27 octubre, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.
24 121
Suscriptores
-324 horas
-517 días
-17030 días
Archivo de publicaciones
سلام
فقط دو روز تا سابمیت نهایی این مقاله باقی مونده دوستانی که نیاز دارن بهم پیام بدن.
@Raminmousa1
🔥 OmniReasoning: Pushing the Limits of Audio-Visual Joint Reasoning
💡 Summary
🔗 Links:
GitHub: https://github.com/huggingface
arXiv: https://arxiv.org/abs/2609.39490
PDF: https://arxiv.org/pdf/2609.39490
Project Page: https://pku-value-lab.github.io/OmniReasoning-Homepage/
📊 Datasets citing this paper:
https://huggingface.co/datasets/mjuicem/OmniReasoning-131K
https://huggingface.co/datasets/mjuicem/OmniReasoningBench
@Machine_learn
Repost from Machine learning books and papers
با عرض سلام این مقاله نفرات ۲ و ۳ رو میخواییم اسم بنویسم. هزینه به ترتیب ۳۰۰ و ۲۰۰ دلار. اگه کسی نیاز داشت به ایدیم پیام بده.
@Raminmousa1
با عرض سلام این مقاله نفرات ۲ و ۳ رو میخواییم اسم بنویسم. هزینه به ترتیب ۳۰۰ و ۲۰۰ دلار. اگه کسی نیاز داشت به ایدیم پیام بده.
@Raminmousa1
🔥 Prism: Dynamic Sparse Attention for Native 2K Joint Video-Audio Generation Model Training
🔗 Links:
GitHub: https://github.com/huggingface
arXiv: https://arxiv.org/abs/2610.05416
PDF: https://arxiv.org/pdf/2610.05416
Project Page: https://francis-rings.github.io/Prism/
🤖 Models citing this paper:
https://huggingface.co/FrancisRing/Prism
🚀 Spaces citing this paper:
https://huggingface.co/spaces/bibibi12345/Prism
https://huggingface.co/spaces/tardellirs/model-pulse
@Machine_learn
🔥 Spatial Memory Intelligence: Endowing World Models with Understanding-Driven Long-Term Memory
🔗 Links:
GitHub: https://github.com/huggingface
arXiv: https://arxiv.org/abs/2610.02521
PDF: https://arxiv.org/pdf/2610.02521
Project Page: https://spatial-memory-intelligence.github.io/
@Machine_learn
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
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📖 "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
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@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
