Machine learning books and papers

Dataset Name: Online Payments Fraud Detection Dataset Basic Description: Online payment fraud big dataset for testing and practice purpose 📖 FULL DATASET DESCRIPTION: The below column reference: 📥 DATASET DOWNLOAD INFORMATION 🔴 Dataset Size: Download dataset as zip (186 MB) 🔰 Direct dataset download link: https://www.kaggle.com/api/v1/datasets/download/rupakroy/online-payments-fraud-detection-dataset @Machine_learn

Dataset Name: Linked In Job Postings (2023 - 2024) Basic Description: LinkedIn Job Postings (2023 - 2024) 📖 FULL DATASET DESCRIPTION: Scraper Code - https://github.com/ArshKA/LinkedIn-Job-Scraper Every day, thousands of companies and individuals turn to LinkedIn in search of talent. This dataset contains a nearly comprehensive record of 124,000+ job postings listed in 2023 and 2024. . 🔰 Direct dataset download link: https://www.kaggle.com/api/v1/datasets/download/arshkon/linkedin-job-postings 📊 Additional information: File count not found Views: 126,000 Downloads: 53,100 📚 RELATED NOTEBOOKS: 1. "Decoding the Job Market: An In-depth Exploration | Upvotes: 84 URL: https://www.kaggle.com/code/pratul007/decoding-the-job-market-an-in-depth-exploration 2. LinkedIn Job Postings 2023 Data Analysis | Upvotes: 58 URL: https://www.kaggle.com/code/enricofindley/linkedin-job-postings-2023-data-analysis @Machine_learn

🔹 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

Dataset Name: Real Life Violence Situations Dataset Basic Description: 1000 videos containing real street fight and 1000 video from other classes 🔴 Dataset Size: Download dataset as zip (4 GB) 🔰 Direct dataset download link: https://www.kaggle.com/api/v1/datasets/download/mohamedmustafa/real-life-violence-situations-dataset 1. Real Time Violence Detection | MobileNet Bi-LSTM | Upvotes: 424 URL: https://www.kaggle.com/code/abduulrahmankhalid/real-time-violence-detection-mobilenet-bi-lstm 2. Real life violence detection using InceptionV3 | Upvotes: 395 URL: https://www.kaggle.com/code/nandinibagga/real-life-violence-detection-using-inceptionv3 3. Real Life Violence Detection / KERAS-TENSORFLOW | Upvotes: 115 URL: https://www.kaggle.com/code/brsdincer/real-life-violence-detection-keras-tensorflow 4. Video Fights Dataset | Upvotes: 24 URL: https://www.kaggle.com/datasets/shreyj1729/cctv-fights-dataset @Machine_learn

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

💻 ++101 Linux commands Open-source eBook 📚 Read @Machine_learn

ایده داری… اما اجراش زمان‌بره؟ هزینه نیرو بالا رفته؟ تولید محتوا کند پیش میره؟ تو وبینار رایگان ایران‌GPU یاد می‌گیری چطور AI ⚡ سرعت کار رو چند برابر می‌کنه ⚡ هزینه‌ها کم میشه ⚡ تیم هوشمندتر کار می‌کنه 📅 ۱۴ دی | ساعت ۱۹ 🎤 پوریا حداد 🎁 فرصت ارزشمند فقط برای شرکت‌کنندگان 💥 تخفیف تا ۴۰٪ روی یک محصول AI ⭐فرصت بردن دو جایزه ۲۰۰ میلیون تومانی لینک ثبت‌نام https://B2n.ir/hw4212

Dataset Name: FIFA23 OFFICIAL DATASET Basic Description: From FIFA17 to FIFA23 statistics for each football player 📖 FULL DATASET DESCRIPTION: The dataset contains +17k unique players and more than 60 columns, general information and all KPIs the famous videogame offers. As the esport scene keeps rising espacially on FIFA, I thought it can be useful for the community (kagglers and/or gamers) 📥 DATASET DOWNLOAD INFORMATION 🔴 Dataset Size: Download dataset as zip (14 MB) 🔰 Direct dataset download link: https://www.kaggle.com/api/v1/datasets/download/bryanb/fifa-player-stats-database 📊 Additional information: File count not found Views: 107,000 Downloads: 66,500 @Machine_learn

Dataset Name: Malaria Bounding Boxes Basic Description: P. vivax (malaria) infected human blood smears 📖 FULL DATASET DESCRIPTION: Malaria is a disease caused by Plasmodium parasites that remains a major threat in global health, affecting 200 million people and causing 400,000 deaths a year. The main species of malaria that affect humans are Plasmodium falciparum and Plasmodium vivax. 📥 DATASET DOWNLOAD INFORMATION 🔴 Dataset Size: Download dataset as zip (5 GB) 🔰 Direct dataset download link: https://www.kaggle.com/api/v1/datasets/download/kmader/malaria-bounding-boxes 📊 Additional information: File count not found Views: 54,400 Downloads: 4,657 @Machine_learn

Python Programming Hans-Petter Halvorsen 📚 Read @Machine_learn

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