Machine learning books and papers

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

☁️ ابرک ویراک را 24 ساعت رایگان تست کنید! قبل از خرید، سرویس ابری VirakCloud را با شرایط واقعی امتحان کنید: 🎁 24 ساعت تست رایگان 🚀 پهنای باند اختصاصی با ترافیک نامحدود ⏱️ پرداخت ساعتی؛ فقط به اندازه مصرفتان هزینه کنید 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: https://t.me/cloud_virak 👇 سپس وارد پنل VirakCloud شوید، کد را وارد کنید و ابرک خود را بسازید: 🔗 https://B2n.ir/qy4432 ☎️ 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

#for_sell @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

فقط نفر دوم از این مقاله باقی مونده...!

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

☁️ 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: 🎁 15% شارژ بیشتر هدیه روی اولین واریزی 🎁 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