Machine learning books and papers

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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn

📈 Analytical overview of Telegram channel Machine learning books and papers

Channel Machine learning books and papers (@machine_learn) in the English language segment is an active participant. Currently, the community unites 24 610 subscribers, ranking 8 079 in the Education category and 13 665 in the Iran region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 610 subscribers.

According to the latest data from 03 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -179 over the last 30 days and by -8 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 15.04%. Within the first 24 hours after publication, content typically collects 2.54% reactions from the total number of subscribers.
Post reach: On average, each post receives 0 views. Within the first day, a publication typically gains 625 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 0.
Thematic interests: Content is focused on key topics such as disorder, psy, مقاله, framework, graph.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn”

Thanks to the high frequency of updates (latest data received on 04 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.

24 610

Subscribers

-824 hours

-737 days

-17930 days

3 700

Post views

~ 62524 hours

~ 93248 hours

15.04%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

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🎬 ساخت ویدیو • Sora • Kling • Veo • Seedance • Lumalabs 🎨 ساخت تصویر • Google Flow • Qwen Image • NanoBanana • ChatGPT Image • Grok 🎤 تقلید صدا • ElevenLabs • Fish Audio • Minimax • Descript • Respeecher 🧠 تحقیق و کاوش • ChatGPT • Gemini • Perplexity • NotebookLM • Deepseek 🗣 ساخت کاراکتر سخنگو • Heygen • Synthesia • D-ID • Hedra ━━━━━━━━━━━━━━━ 🔗 لینک ابزارها: • ChatGPT → https://chatgpt.com • Gemini → https://gemini.google.com • Perplexity → https://perplexity.ai • Deepseek → https://deepseek.com • NotebookLM → https://notebooklm.google.com • Kling → https://klingai.com • Veo → https://deepmind.google/technologies/veo • Lumalabs → https://lumalabs.ai • Sora → https://openai.com/sora • ElevenLabs → https://elevenlabs.io • Fish Audio → https://fish.audio • Descript → https://descript.com • Heygen → https://heygen.com • Synthesia → https://synthesia.io • D-ID → https://d-id.com @ai_farshad

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تنها ۳ روز تا سابمیت این مقاله باقی مونده....!

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برای این مقاله فقط ۵ روز وقت داریم دوستانی که نیاز دارند زودتر اقدام کنن...!

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با عرض سلام یکی از مقالاتمون در حوزه ی wound image classification در ژورنال nature scientific reports ریوایزد خورده و جایگاه های ۲، ۴ و ۵ اش قابل اضافه شدن می باشد. دوستانی که نیاز دارن می تونن جهت ثبت اسم به ایدی بنده پیام بدن Price 2: 300$ 4: 200$ 5:150$ @Raminmousa @Paper4money @Machine_learn

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Repost from Papers

با عرض سلام یکی از مقالاتمون در حوزه ی wound image classification در ژورنال nature scientific reports ریوایزد خورده و جایگاه های ۲، ۴ و ۵ اش قابل اضافه شدن می باشد. دوستانی که نیاز دارن می تونن جهت ثبت اسم به ایدی بنده پیام بدن Price 2: 300$ 4: 200$ 3:150$ @Raminmousa @Paper4money @Machine_learn

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Repost from Machine Learning with Python

Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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🔥 Awesome open-source project to learn more about Transformer Models! 🤖✨ We found this interactive website that shows you visually how transformer models work. 🌐📊 Transformer Explainer: https://poloclub.github.io/transformer-explainer/ @Machine_learn

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❣️

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با عرض سلام دوستان اوضاع اصلا جالب نیست و کلا اینترنت نداریم. امیدوارم حالتون خوب باشه و اوضاع بهتر بشه. 🖤

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Fri, 27 Feb 2026 (showing first 50 of 206 entries ) [1] arXiv:2602.23352 [pdf, html, other] Stark localization of interacting particles Wojciech De Roeck, Amirali Hannani, Alessio Lerose, Nathan Vandenbosch Subjects: Mathematical Physics (math-ph); Disordered Systems and Neural Networks (cond-mat.dis-nn) [2] arXiv:2602.23350 [pdf, html, other] A strengthening of the dimensional Brunn-Minkowski conjecture implies the (B)-Conjecture Sotiris Armeniakos, Jacopo Ulivelli Comments: Comments are welcome! Subjects: Functional Analysis (math.FA); Metric Geometry (math.MG) [3] arXiv:2602.23343 [pdf, html, other] Cyclic sieving for a class of rectangular domino tableaux Laura Colmenarejo, Bridget Eileen Tenner, Camryn E. Thompson Comments: 17 pages Subjects: Combinatorics (math.CO) [4] arXiv:2602.23340 [pdf, html, other] Combinatorial Properties of the Raisonnier Filter Spyridon Dialiatsis, Yurii Khomskii Subjects: Logic (math.LO) [5] arXiv:2602.23326 [pdf, html, other] Spin Glass Concepts in Computer Science, Statistics, and Learning Andrea Montanari Comments: 33 pages; 2 pdf figures Subjects: Probability (math.PR); Disordered Systems and Neural Networks (cond-mat.dis-nn) [6] arXiv:2602.23325 [pdf, html, other] Spanning tight components in 4-uniform hypergraphs Francesco Di Braccio, Brian Hearn, Joanna Lada, Mihir Neve, Lu-Ming Zhang Comments: 24 pages, 4 figures Subjects: Combinatorics (math.CO) [7] arXiv:2602.23323 [pdf, html, other] Modeling Large-Scale Adversarial Swarm Engagements using Optimal Control Claire Walton, Isaac Kaminer, Qi Gong, Abram H. Clark, Theodoros Tsatsanifos Comments: arXiv admin note: substantial text overlap with arXiv:2108.02311. substantial text overlap with arXiv:2108.02311 Subjects: Optimization and Control (math.OC) @Machine_learn

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فقط نفرات ۲ و ۳ از این مقاله باقی موندن...!

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✨RoboCurate: Harnessing Diversity with Action-Verified Neural Trajectory for Robot Learning 📝 Summary: RoboCurate enhances synthetic robot learning data by evaluating action quality through simulator replay consistency. It also augments observation diversity via image editing and video transfer techniques. This leads to substantial improvements in robot task success rates compared to using real da... 🔹 Publication Date: Published on Feb 21 🔹 Paper Links: • arXiv Page: https://arxiv.org/abs/2602.18742 • PDF: https://arxiv.org/pdf/2602.18742 • Project Page: https://seungkukim.github.io/robocurate/ @Machine_learn

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Repost from Papers

با عرض سلام مي خواهيم مقاله كنفرانسي با عنوان زير بنويسيم Complex Sig: Complex deep model for signal classification Abstract: The ability to classify signals is an important task that provides the opportunity for many different applications. In the early research for signal classification, they had to decompose the signal using FT (Fourier transform), SIFT, MFCC or other manual methods using statistical modulation features, then classify these signals by a traditional machine learning approach. In the last few years, the process of learning deep models that lead to the automatic extraction of features has positively affected classification. Different deep-learning models with di erent depths have been proposed in the literature. This article proposes different approaches to classify signals in different SNR conditions. ResNet-based approaches perform well for high SNRs but poorly when dealing with low SNRs. Therefore, TRansforme-based approaches were proposed for classification, reaching an average accuracy of 0.7056 in low SNR and an average of 0.9089 in high SNR. نتايج اوليه خوب بوده و قابل مقايسه با ساير مقالات تو اين حوزه ميباشد. نياز به ٢ يا سه نفر داريم كه مشاركت كنند. Price: 2: 300$ 3:200$ 4:100$ @Raminmousa @paper4money @Machine_learn

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هر ثانیه توقف، یعنی از دست رفتن زمان، هزینه و فرصت… پایداری دیگر یک انتخاب نیست؛ یک ضرورت است. 🚀 ایران‌GPU؛جایی که پروژه‌ها متوقف نمی‌شوند. 🏛 تنها و اولین شرکت بورسی هوش مصنوعی ایران 🕒 بیش از ۵ سال سابقه فعالیت حرفه‌ای 🌐 شبکه‌ای از ۲۰+ دیتاسنتر غیرمتمرکز در سراسر کشور 🧠 مناسب تیم‌ها، پژوهشگران و سازمان‌های حرفه‌ای AI 🛟 پشتیبانی ۲۴ ساعته، ۷ روز هفته 📈 تضمین SLA با دسترس‌پذیری 99.9٪ و ارائه سرور داخل ایران 📩 ثبت درخواست مشاوره | شروع مسیر هوشمندانه https://b2n.ir/qk8423

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🔹 Title: Predicting the Order of Upcoming Tokens Improves Language Modeling 🔹 Publication Date: Published on Aug 26 🔹 Paper Links: • arXiv Page: https://arxiv.org/abs/2508.19228 • PDF: https://arxiv.org/pdf/2508.19228 • Github: https://github.com/zaydzuhri/token-order-prediction @Machine_learn

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🔹 Title: CODA: Coordinating the Cerebrum and Cerebellum for a Dual-Brain Computer Use Agent with Decoupled Reinforcement Learning 🔹 Publication Date: Published on Aug 27 🔹 Paper Links: • arXiv Page: https://arxiv.org/abs/2508.20096 • PDF: https://arxiv.org/pdf/2508.20096 • Project Page: https://github.com/OpenIXCLab/CODA • Github: https://github.com/OpenIXCLab/CODA @Machine_learn

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رمضان الکریم ❤️ @Machine_learn

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Dataset Name: Gallstone Dataset (UCI) Basic Description: Gallstone Dataset (UCI Machine Learning Repository) 📥 DATASET DOWNLOAD INFORMATION ================================== 🔴 Dataset Size: Download dataset as zip (81 kB) 🔰 Direct dataset download link: URL not found 📊 Additional information: ================================== File count not found Views: 1,128 Downloads: 246 📚 RELATED NOTEBOOKS: ================================== 1. Heart Attack Risk Prediction Dataset | Upvotes: 274 URL: https://www.kaggle.com/datasets/iamsouravbanerjee/heart-attack-prediction-dataset @Machine_learn

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How we made Python's packaging library 3x faster 📚 Read @Machine_learn

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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