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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
Breaking the Sorting Barrier for Directed Single-Source Shortest Paths 📚 link @Machine_learn
Breaking the Sorting Barrier for Directed Single-Source Shortest Paths 📚 link @Machine_learn

Title: Personalized Safety Alignment for Text-to-Image Diffusion Models Paper Links: • arXiv Page: https://arxiv.org/abs/2508
Title: Personalized Safety Alignment for Text-to-Image Diffusion Models Paper Links: • arXiv Page: https://arxiv.org/abs/2508.01151 • PDF: https://arxiv.org/pdf/2508.01151 • Github: https://m-e-agi-lab.github.io/PSAlign/ @Machine_learn

CoAct-1: Computer-using Agents with Coding as Actions 📚 Read @Machine_learn
CoAct-1: Computer-using Agents with Coding as Actions 📚 Read @Machine_learn

DSPy SIMBA explained 📚 Link @Machine_learn
DSPy SIMBA explained 📚 Link @Machine_learn

🔹 Title: ReMoMask: Retrieval-Augmented Masked Motion Generation 🔹 Paper Links: • arXiv Page: https://arxiv.org/abs/2508.026
🔹 Title: ReMoMask: Retrieval-Augmented Masked Motion Generation 🔹 Paper Links: • arXiv Page: https://arxiv.org/abs/2508.02605 • PDF: https://arxiv.org/pdf/2508.02605 • Project Page: https://aigeeksgroup.github.io/ReMoMask/ • Github: https://github.com/AIGeeksGroup/ReMoMask @Machin_learn

Mathematical theory of deep learning 📚 Book @Machine_learn
Mathematical theory of deep learning 📚 Book @Machine_learn

Matrix Calculus (for Machine Learning and Beyond) Link @Machine_learn
Matrix Calculus (for Machine Learning and Beyond) Link @Machine_learn

Repost from N/a
با عرض سلام سه مقاله تحت ريوايز داريم حداقل مي تونيم ٥ سايت به هر كدوم اضافه كنيم حوزه هاي LLM و Medical قابل اضافه شدن هستن. @Raminmousa @papercite

Article Title: OmniConsistency: Learning Style-Agnostic Consistency from Paired Stylization Data PDF Download Link: https://arxiv.org/pdf/2505.18445v1.pdf GitHub:https://github.com/showlab/omniconsistency @Machine_learn

An introduction to the symmetric group algebra 📚 link @Machine_learn
An introduction to the symmetric group algebra 📚 link @Machine_learn

🎥 RNA-Seq Data Analysis in R: An Effective Step-by-Step Guide 🎞 Watch @Machine_learn

Deep Research Agents with Test-Time Diffusion Google keeps pushing on diffusion. 📚Paper @Machine_learn
Deep Research Agents with Test-Time Diffusion Google keeps pushing on diffusion. 📚Paper @Machine_learn

Repost from Papers
با عرض سلام نفرات ۴ و ۵ این پروژه قابل اضافه شدن است و علاوه بر تسک هزینه مشارکت نیز گرفته می‌شود. 4:250$ 5:200$ جهت مشارکت ب
با عرض سلام نفرات ۴ و ۵ این پروژه قابل اضافه شدن است و علاوه بر تسک هزینه مشارکت نیز گرفته می‌شود. 4:250$ 5:200$ جهت مشارکت به ایدی بنده پیام بدین. @Raminmousa

The Era of DiffusionLM might be upon us 📚 Link @Machine_learn
The Era of DiffusionLM might be upon us 📚 Link @Machine_learn

AI Comes Up with Bizarre Physics Experiments. But They Work. 👉 Read @Machine_learn
AI Comes Up with Bizarre Physics Experiments. But They Work. 👉 Read @Machine_learn

Anthropic just released a research paper. Inverse Scaling in Test-Time Compute 📚 link @Machine_learn
Anthropic just released a research paper. Inverse Scaling in Test-Time Compute 📚 link @Machine_learn

How to Train Your LLM Web Agent: A Statistical Diagnosis 📕 Link @Machine_learn
How to Train Your LLM Web Agent: A Statistical Diagnosis 📕 Link @Machine_learn

Aipython 📕 Books @Machine_learn
Aipython 📕 Books @Machine_learn

فقط دو نفر باقی مونده این کار دوستانی که نیاز دارن به بنده مراجعه کنند.!!! @Raminmousa

Step-by-Step Diffusion: An Elementary Tutorial 📚 Read @Machine_learn
Step-by-Step Diffusion: An Elementary Tutorial 📚 Read @Machine_learn