Machine learning books and papers

Video-LMM Post-Training: A Deep Dive into Video Reasoning with Large Multimodal Models Read @Machine_learn

با عرض سلام برای مقاله زیر نیاز به نفر ۳ داریم. KG-Psy: A Knowledge-Graph and GPT-5 Based Framework for Personalized Clinical Decision Support in Bipolar Disorder and Borderline Personality Disorder   Abstract: Accurate diagnosis and personalized treatment planning for complex psychiatric disorders such as Bipolar Disorder (BD) and Borderline Personality Disorder (BPD) remain major challenges due to overlapping symptoms, fluctuating mood patterns, and heterogeneous clinical presentations. To address these challenges, we introduce KG-Psy, a hybrid neuro-symbolic framework that combines a domain-specific psychiatric Knowledge Graph (KG) with the advanced reasoning capabilities of GPT-5. KG-Psy constructs multi-layer psychiatric knowledge graphs encoding symptom trajectories, neural correlates, pharmacological mechanisms, therapeutic guidelines, comorbidities, and behavioral patterns extracted from large-scale clinical literature. GPT-5 is employed to extract clinical entities, infer latent symptom-neural relationships, assess diagnostic likelihoods, and generate patient-specific treatment recommendations. The integration of structured KG reasoning with LLM-based inference allows KG-Psy to produce interpretable, evidence-supported, and clinically actionable outputs. We evaluated KG-Psy on 310 de-identified psychiatric case reports and 12 expert-validated benchmark scenarios. The framework achieved 91.5% F1-score in distinguishing BD from BPD and an average pathway confidence of 86.9%, indicating robust multi-step inference. In personalized treatment recommendation tasks, KG-Psy achieved 88.7% accuracy, outperforming LLM-only and KG-only baselines by 23% and 31%, respectively. ....   Keywords: Bipolar Disorder, Borderline Personality Disorder, Knowledge Graph, GPT-5, Personalized Treatment 3 :15 milion @Raminmousa @Machine_learn @paper4money

🔹 Title: Forecasting Probability Distributions of Financial Returns with Deep Neural Networks 🔹 Publication Date: Published on Aug 26 🔹 Paper Links: • arXiv Page: https://arxiv.org/abs/2508.18921 • PDF: https://arxiv.org/pdf/2508.18921 • Github: https://github.com/jmichankow/deep_learning_probability @Machine_learn

🚀 هوش مصنوعی در عمل | ویژه کسب‌وکارها اگه می‌خوای AI رو وارد کسب و کارت کنی، این وبینار رو از دست نده 👇 از مهندسی پرامپت و ساخت ایجنت‌ها و دستیارهای هوشمند تا ابزارهای کاربردی و پول‌ساز هوش مصنوعی و روش‌های درآمدزایی ریالی و دلاری با AI 🔹 سیستم‌سازی هوشمند در کسب‌وکار 🔹 با کمترین هزینه 🔹 کاملاً عملی و قابل اجرا 📌 لینک ثبت‌نام وبینار👇 https://B2n.ir/fm2539 منتظرتون هستیم 🌱

🛠️OpenAI just released new guide on how coding agents like GPT-5.1-Codex-Max plug into everyday engineering workflow 📚 Read @Machine_learn

Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. https://t.me/CodeProgrammer

🔹 Title: ReportBench: Evaluating Deep Research Agents via Academic Survey Tasks 🔹 Publication Date: Published on Aug 14 🔹 Paper Links: • arXiv Page: https://arxiv.org/abs/2508.15804 • PDF: https://arxiv.org/pdf/2508.15804 @Machine_learn

🔹 Title: ObjFiller-3D: Consistent Multi-view 3D Inpainting via Video Diffusion Models 🔹 Publication Date: Published on Aug 25 🔹 Paper Links: • arXiv Page: https://arxiv.org/abs/2508.18271 • PDF: https://arxiv.org/pdf/2508.18271 • Project Page: https://objfiller3d.github.io/ • Github: https://github.com/objfiller3d/ObjFiller-3D @Machine_learn

دوستان برای این مقاله نیاز به نفرات ۴ و ۵ داریم Title: Recurrent Neural Networks Basic deficiencies: NP-complet feature order Abstract: The problem of time series prediction analyzes patterns in past data to predict the future. Traditional machine learning algorithms, despite achieving impressive results, require manual feature selection. Automatic feature selection along with the addition of time concept in deep recurrent networks has led to the provision of more suitable solutions. The selection of feature order in deep recurrent networks leads to the provision of different results due to the use of Back-propagation. The problem of selecting feature order is an NP-complete problem. In this research, the aim is to provide a solution to improve this problem. .... Price: 4:300$ 5:200$ @Raminmousa @Machine_learn @paper4money

با عرض سلام برای مقاله زیر نیاز به نفرات ۲ و ۳ داریم. KG-Psy: A Knowledge-Graph and GPT-5 Based Framework for Personalized Clinical Decision Support in Bipolar Disorder and Borderline Personality Disorder   Abstract: Accurate diagnosis and personalized treatment planning for complex psychiatric disorders such as Bipolar Disorder (BD) and Borderline Personality Disorder (BPD) remain major challenges due to overlapping symptoms, fluctuating mood patterns, and heterogeneous clinical presentations. To address these challenges, we introduce KG-Psy, a hybrid neuro-symbolic framework that combines a domain-specific psychiatric Knowledge Graph (KG) with the advanced reasoning capabilities of GPT-5. KG-Psy constructs multi-layer psychiatric knowledge graphs encoding symptom trajectories, neural correlates, pharmacological mechanisms, therapeutic guidelines, comorbidities, and behavioral patterns extracted from large-scale clinical literature. GPT-5 is employed to extract clinical entities, infer latent symptom-neural relationships, assess diagnostic likelihoods, and generate patient-specific treatment recommendations. The integration of structured KG reasoning with LLM-based inference allows KG-Psy to produce interpretable, evidence-supported, and clinically actionable outputs. We evaluated KG-Psy on 310 de-identified psychiatric case reports and 12 expert-validated benchmark scenarios. The framework achieved 91.5% F1-score in distinguishing BD from BPD and an average pathway confidence of 86.9%, indicating robust multi-step inference. In personalized treatment recommendation tasks, KG-Psy achieved 88.7% accuracy, outperforming LLM-only and KG-only baselines by 23% and 31%, respectively. ....   Keywords: Bipolar Disorder, Borderline Personality Disorder, Knowledge Graph, GPT-5, Personalized Treatment  2 :20 milion 3 :15 milion @Raminmousa @Machine_learn @paper4money

Rethinking JEPA: Compute-Efficient Video SSL with Frozen Teachers 📕 Link @Machine_learn

Advanced, Overlooked Python Typing 📚 Read @Machine_learn

با عرض سلام مقاله زیر جهت ثبت اسم اماده ی ارسال Title: Recurrent Neural Networks Basic deficiencies: NP-complet feature order Abstract: The problem of time series prediction analyzes patterns in past data to predict the future. Traditional machine learning algorithms, despite achieving impressive results, require manual feature selection. Automatic feature selection along with the addition of time concept in deep recurrent networks has led to the provision of more suitable solutions. The selection of feature order in deep recurrent networks leads to the provision of different results due to the use of Back-propagation. The problem of selecting feature order is an NP-complete problem. In this research, the aim is to provide a solution to improve this problem. .... Price: 2: 500$ 3:400$ 4:300$ 5:200$ @Raminmousa @Machine_learn @paper4money

📑 A comprehensive review of cluster methods for drug–drug interaction network 📎 Study the paper @Machine_learn

Python Programming for Economics and Finance 📚 Book @Machine_learn

دوستانی که می خوان تو حوزه ی LLM مقاله داشته باشن می تونن تو این مقاله شرکت کنند.

با عرض سلام برای مقاله زیر نیاز به نفرات ۲ و ۳ داریم. KG-Psy: A Knowledge-Graph and GPT-5 Based Framework for Personalized Clinical Decision Support in Bipolar Disorder and Borderline Personality Disorder   Abstract: Accurate diagnosis and personalized treatment planning for complex psychiatric disorders such as Bipolar Disorder (BD) and Borderline Personality Disorder (BPD) remain major challenges due to overlapping symptoms, fluctuating mood patterns, and heterogeneous clinical presentations. To address these challenges, we introduce KG-Psy, a hybrid neuro-symbolic framework that combines a domain-specific psychiatric Knowledge Graph (KG) with the advanced reasoning capabilities of GPT-5. KG-Psy constructs multi-layer psychiatric knowledge graphs encoding symptom trajectories, neural correlates, pharmacological mechanisms, therapeutic guidelines, comorbidities, and behavioral patterns extracted from large-scale clinical literature. GPT-5 is employed to extract clinical entities, infer latent symptom-neural relationships, assess diagnostic likelihoods, and generate patient-specific treatment recommendations. The integration of structured KG reasoning with LLM-based inference allows KG-Psy to produce interpretable, evidence-supported, and clinically actionable outputs. We evaluated KG-Psy on 310 de-identified psychiatric case reports and 12 expert-validated benchmark scenarios. The framework achieved 91.5% F1-score in distinguishing BD from BPD and an average pathway confidence of 86.9%, indicating robust multi-step inference. In personalized treatment recommendation tasks, KG-Psy achieved 88.7% accuracy, outperforming LLM-only and KG-only baselines by 23% and 31%, respectively. ....   Keywords: Bipolar Disorder, Borderline Personality Disorder, Knowledge Graph, GPT-5, Personalized Treatment  2 :20 milion 3 :15 milion @Raminmousa @Machine_learn @paper4money