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Machine learning books and papers

Machine learning books and papers

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📈 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 24 533 subscribers, ranking 8 070 in the Education category and 13 771 in the Iran region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 533 subscribers.

According to the latest data from 22 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -150 over the last 30 days and by -5 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 7.45%. 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 1 829 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 3.
  • 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 23 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.

24 533
Subscribers
-524 hours
-417 days
-15030 days
Posts Archive
Audio, Video, and Webcams in Python (Lost Chapter from Automate the Boring Stuff) 📕 Book @Machine_learn
Audio, Video, and Webcams in Python (Lost Chapter from Automate the Boring Stuff) 📕 Book @Machine_learn

با عرض سلام موضوع زير رو می خواهیم به صورت گروهی ادامه بدیم. - survey on GAN methods for time series data generation نفرات ١ تا ٣ پر شده و ٤ ،٥، و ٦ باقي موندن. دوستاني كه نياز دارند مي تونن به بنده پيام بدن. نمونه كارهاي مشترك قبلي 1: llm survey https://www.preprints.org/manuscript/202504.2464/download/final_file 2: Next G survey https://www.preprints.org/frontend/manuscript/6a1dadfbe284a8e1b45ba04e5af1fdf5/download_pub 3: Metrcis survey. @Raminmousa

سابميت اين مقاله امشب اگر كسي نياز داشت نفر سوم خالي هستش...! @Raminmousa

Reflections on OpenAI 📚 link @Machine_learn

How to Optimize Your Python Code Even If You’re a Beginner 📚 Read @Machine_learn

این مقاله تا فرداشب سابمیت میشه دوستانی که نیاز دارن بهم مراجعه کنند...! @Raminmousa

Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trends 🖥 Github: https://githu
Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trends 🖥 Github: https://github.com/aoqunjin/awesome-vla-post-training 📕 Paper: https://arxiv.org/pdf/2506.20966v1.pdf 🔗 Dataset: https://paperswithcode.com/dataset/imagenet1 @Machine_learn

Repost from Papers
با عرض سلام مقاله مروري تحت عنوان زير رو نوشتيم فردا قرار سابميت بشه اگر كسي از دوستان خواست ميتونه براي جايگاه سوم اقدام كنه
با عرض سلام مقاله مروري تحت عنوان زير رو نوشتيم فردا قرار سابميت بشه اگر كسي از دوستان خواست ميتونه براي جايگاه سوم اقدام كنه. Survey on evaluation metrics for learning system @Raminmousa @paper4money

Understanding Gradients 📚 Read @Machine_learn
Understanding Gradients 📚 Read @Machine_learn

Article Title: AutoAgent: A Fully-Automated and Zero-Code Framework for LLM Agents PDF Download Link: https://arxiv.org/pdf/2502.05957v2.pdf GitHub:https://github.com/hkuds/autoagenthttps://github.com/hkuds/auto-deep-research @Machine_learn

Microsoft Build 2025: The age of AI agents and building the open agentic web 📚 Read @Machine_learn
Microsoft Build 2025: The age of AI agents and building the open agentic web 📚 Read @Machine_learn

Repost from Papers
با عرض سلام نيازمند شخصي هستيم كه بتونه در پروژه MedicalRec به ما كمك كنه. Git:https://github.com/Ramin1Mousa/MedicalRec هدف
با عرض سلام نيازمند شخصي هستيم كه بتونه در پروژه MedicalRec به ما كمك كنه. Git:https://github.com/Ramin1Mousa/MedicalRec هدف اين پروژه ارائه ي يك ريكامندر در حوزه پزشكي است كه از ترين مجدد شبكه ها جلوگيري كنه. كه منجر به صرف جوي در هزينه و صرف جوي در انرژي مصرفي ميشه. مجموعه داده ها شامل ٣٠٠٠ مقاله بوده كه كامل طي ٣ ماه جمع اوري شده است. هزينه مشاركت ٥٠٠$ هستش و اسم به عنوان نفر دوم در نظر گرفته ميشه. Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence If: 18.6 @Raminmousa

📄 Large Language Models in Genomics—A Perspective on Personalized Medicine 📎 Study the paper @Machine_learn
📄 Large Language Models in GenomicsA Perspective on Personalized Medicine 📎 Study the paper @Machine_learn

SRT-H: A Hierarchical Framework for Autonomous Surgery via Language Conditioned Imitation Learning 📚 Read @Machine_learn
SRT-H: A Hierarchical Framework for Autonomous Surgery via Language Conditioned Imitation Learning 📚 Read @Machine_learn

با عرض سلام این یک کار بنیادی هستش که در ادامه مقالات متفاوتی در این حوزه خواهیم داد. نفر دوم اگر کسی تمایل داشت اطلاع بده. @Raminmousa

با عرض سلام نيازمند شخصي هستيم كه بتونه در پروژه MedicalRec به ما كمك كنه. Git:https://github.com/Ramin1Mousa/MedicalRec هدف اين پروژه ارائه ي يك ريكامندر در حوزه پزشكي است كه از ترين مجدد شبكه ها جلوگيري كنه. كه منجر به صرف جوي در هزينه و صرف جوي در انرژي مصرفي ميشه. مجموعه داده ها شامل ٣٠٠٠ مقاله بوده كه كامل طي ٣ ماه جمع اوري شده است. هزينه مشاركت ٥٠٠$ هستش و اسم به عنوان نفر دوم در نظر گرفته ميشه. Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence If: 18.6 @Raminmousa

Article Title: AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning PDF Download Link: https://arxiv.org/pdf/2505.24298v2.pdf GitHub:https://github.com/inclusionai/areal Datasets: • MATH @Machine_learn

A Beginner’s Guide to AirTable for Data Analysis 📚 Guide @Machine_learn
A Beginner’s Guide to AirTable for Data Analysis 📚 Guide @Machine_learn

Context Engineering Guide 📚 Read @Machine_learn
Context Engineering Guide 📚 Read @Machine_learn

Repost from Papers
با عرض سلام نفرات ٢ تا ٤ قابل اضافه شدن به مقاله زير مي باشد. Title:Probability latent for Recurrent Neural Networks Basic de
با عرض سلام نفرات ٢ تا ٤ قابل اضافه شدن به مقاله زير مي باشد. Title:Probability latent for Recurrent Neural Networks Basic deficiencies abstract: 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 the time concept in deep recurrent networks has led to 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. . ..... The proposed approach has an improvement of 0.49 over the reviewed approaches in some benchmarks. Price: 2:500$ 3:400$ 4:250$ @Raminmousa @Machine_learn @Paper4money