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 24 518 subscribers, ranking 8 048 in the Education category and 13 749 in the Iran region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 518 subscribers.

According to the latest data from 25 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -164 over the last 30 days and by -1 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 7.13%. Within the first 24 hours after publication, content typically collects 1.90% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 748 views. Within the first day, a publication typically gains 465 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 1.
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 26 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.

24 518

Subscribers

-124 hours

-407 days

-16430 days

1 748

Post views

~ 46524 hours

~ 57948 hours

7.13%

Engagement rate

No data

Posts per day

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Channel Posts

⚡️Lumine: An Open Recipe for Building Generalist Agents in 3D Open Worlds HF: https://huggingface.co/papers/2511.08892 Peoject: https://www.lumine-ai.org/ Paper: https://arxiv.org/abs/2511.08892 @Machine_learn

2	🔥 World Action Models: A Survey 💡 The paper World Action Models A Survey provides a comprehensive overview of World Action Models, which are predictive action systems that generate future states for decision making. These models balance representational richness against computational constraints, and recent developments have led to a blurring of boundaries among various related models. The survey aims to clarify these boundaries and provide a common account of the field. The authors organize existing works into two complementary views. The first view examines what each method is required to generate, including rendered futures, latent futures, and video generation free action reasoning. The second view decomposes each method into its predictive substrate, backbone, action coupling, and deployment regime. This anatomy allows for a unified discussion of key aspects such as interactability, causality, persistence, physical plausibility, and generalization. The survey reveals a consistent design pattern in World Action Models, where design choices trade representational richness against compute, memory, latency, and action label cost. The authors find that the field is moving towards methods that generate less of the future while preserving what is required for control. The survey provides a clear and unified account of the field, covering data, evaluation, and open challenges, and provides a foundation for future research in World Action Models. The main contributions of the paper are to clarify the boundaries and definitions of World Action Models, to provide a comprehensive overview of existing works, and to identify a consistent design pattern in the field. The survey also highlights the key challenges and open issues in World Action Models, including the need for more efficient and effective methods that balance representational richness against computational constraints. Overall, the paper provides a valuable resource for researchers and practitioners in the field of World Action Models, and helps to advance the state of the art in predictive action systems. 📅 Published on Jun 18 🔗 Links: • GitHub: https://github.com/huggingface • arXiv: https://arxiv.org/abs/2606.20781 • PDF: https://arxiv.org/pdf/2606.20781 • Project Page: https://world-action-models.github.io/ ━━━━━━━━━━━━━━━━━━━━━━━━ @Machine_learn	467
3	🔥 Efficient Guided Generation for Large Language Models 💡 The paper presents an efficient method for guiding large language model text generation using regular expressions and context-free grammars. The problem addressed is that guided generation can be impractical due to significant overhead. The authors propose an approach that adds minimal overhead to the token sequence generation process. This method makes guided generation feasible in practice. The approach is implemented in the open source Python library Outlines, providing a practical solution for efficient guided generation. The results indicate that the method is effective, allowing for guided generation with little to no overhead, which is a significant contribution to the field of natural language processing. 📅 Published on Jul 19, 2023 🔗 Links: • GitHub: https://github.com/huggingface • arXiv: https://arxiv.org/abs/2307.09702 • PDF: https://arxiv.org/pdf/2307.09702 ━━━━━━━━━━━━━━━━━━━━━━━━ @Machine_learn	464
4	با عرض سلام یکی از مقالاتمون در حوزه ی wound image classification در ژورنال nature scientific reports ریوایزد خورده و جایگاه های ۲ 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$ 5:150$ @Raminmousa @Paper4money @Machine_learn	1 123
5	📃 Current Bioinformatics Tools in Precision Oncology 📎 Study paper @Machine_learn	1 100
6	با عرض سلام مقاله MedicalRec توسط بنده و دوستان ارائه شد. این مقاله جهت ارائه ی سیستم پیشنهاد دهنده مدل طبقه بندی برای تصاویر پزشکی میباشد. در ادامه ما می خواهیم MedicalRec2 را توسعه دهیم که یک مدل پیشنهاد دهنده طبقه بند و تقسیم بند در حوزه ی پزشکی می باشد. از این رو نفرات ۲ تا ۶ این مقاله را جهت مشارکت در نظر داریم. هزینه ها از قرار زیر می باشند. 2: 500$ 3: 400$ 4: 300$ 5: 250$ 6: 200$ جهت مشارکت با ایدی بنده در ارتباط باشین. @Raminmousa @Paper4money	1 748
7	+5 🎬 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	2 593
8	تنها ۳ روز تا سابمیت این مقاله باقی مونده....!	2 064
9	برای این مقاله فقط ۵ روز وقت داریم دوستانی که نیاز دارند زودتر اقدام کنن...!	634
10	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	2 852
11	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	480
12	Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A	2 834
13	🔥 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	3 002
14	❣️	3 932

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