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Machine learning books and papers

Machine learning books and papers

前往频道在 Telegram

📈 Telegram 频道 Machine learning books and papers 的分析概览

频道 Machine learning books and papers (@machine_learn) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 24 596 名订阅者,在 教育 类别中位列第 8 075,并在 伊朗 地区排名第 13 741

📊 受众指标与增长动态

невідомо 创建以来,项目保持高速增长,吸引了 24 596 名订阅者。

根据 12 六月, 2026 的最新数据,频道保持稳定运转。过去 30 天订阅人数变化为 -148,过去 24 小时变化为 -2,整体触达仍然可观。

  • 认证状态: 未认证
  • 互动率 (ER): 平均受众互动率为 5.60%。内容发布后 24 小时内通常能获得 N/A% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 1 378 次浏览,首日通常累积 0 次浏览。
  • 互动与反馈: 受众积极参与,单帖平均反应数为 4
  • 主题关注点: 内容集中在 disorder, psy, مقاله, framework, graph 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台:
Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn

凭借高频更新(最新数据采集于 13 六月, 2026),频道始终保持新鲜度与高覆盖。分析显示受众积极互动,使其成为 教育 类别中的关键影响点。

24 596
订阅者
-224 小时
-77
-14830
帖子存档
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

🔹 Title: Mind the Third Eye! Benchmarking Privacy Awareness in MLLM-powered Smartphone Agents 🔹 Publication Date: Published
🔹 Title: Mind the Third Eye! Benchmarking Privacy Awareness in MLLM-powered Smartphone Agents 🔹 Publication Date: Published on Aug 27 🔹 Paper Links: • arXiv Page: https://arxiv.org/abs/2508.19493 • PDF: https://arxiv.org/pdf/2508.19493 • Project Page: https://zhixin-l.github.io/SAPA-Bench • Github: https://github.com/Zhixin-L/SAPA-Bench @Machine_learn

🔹 Title: Self-Rewarding Vision-Language Model via Reasoning Decomposition 🔹 Publication Date: Published on Aug 27 🔹 Paper
🔹 Title: Self-Rewarding Vision-Language Model via Reasoning Decomposition 🔹 Publication Date: Published on Aug 27 🔹 Paper Links: • arXiv Page: https://arxiv.org/abs/2508.19652 • PDF: https://arxiv.org/pdf/2508.19652 @Machine_learn

سلام اين مقاله امشب سابميت ميشه اگر از دوستان كسي نياز داشت با من هماهنگ بشه @Raminmousa

با عرض سلام ما برای این مقاله نیاز به نفر دوم داریم و تنها مقاله دو نفر جایگاه داره. دوستانی که نیاز دارن می تونن به پی وی بنده پیام بدن @Raminmousa ⚠️ فردا اخرین مهلت ...!

با عرض سلام ما برای این مقاله نیاز به نفر دوم داریم و تنها مقاله دو نفر جایگاه داره. دوستانی که نیاز دارن می تونن به پی وی بنده پیام بدن @Raminmousa

Repost from Papers
Title: Fundamental Challenges of Neural Network in Handling Sequential Feature of Time Series: Np-hard Challenge Journal: IEE
Title: Fundamental Challenges of Neural Network in Handling Sequential Feature of Time Series: Np-hard Challenge Journal: IEEE transaction on soft computing Author : 2 Price: 1200 USDT @Raminmousa @Machine_learn @Paper4money

🔹 Title: Select to Know: An Internal-External Knowledge Self-Selection Framework for Domain-Specific Question Answering 🔹 P
🔹 Title: Select to Know: An Internal-External Knowledge Self-Selection Framework for Domain-Specific Question Answering 🔹 Publication Date: Published on Aug 21 🔹 Paper Links: • arXiv Page: https://arxiv.org/abs/2508.15213 • PDF: https://arxiv.org/pdf/2508.15213 @Machine_learn

Sharing State Between Prompts and Programs 📚 Read @Machine_learn
Sharing State Between Prompts and Programs 📚 Read @Machine_learn

Dataset Name: Online Payments Fraud Detection Dataset Basic Description: Online payment fraud big dataset for testing and pra
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 DES
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
🔹 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 vide
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

Repost from Papers
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
💻 ++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
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 DA
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 DESCRI
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
Python Programming Hans-Petter Halvorsen 📚 Read @Machine_learn