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 24 522 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 24 522 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.

24 522

Subscribers

-524 hours

-417 days

-15030 days

1 829

Post views

~ 46524 hours

~ 57948 hours

7.45%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

24 522

NVIDIA just open sourced Open Code Reasoning models - 32B, 14B AND 7B - APACHE 2.0 licensed 🔥 > Beats O3 mini & O1 (low) on LiveCodeBench 😍 Backed by OCR dataset the models are 30% token efficient than other equivalent Reasoning models Works with llama.cpp, vLLM, transformers, TGI and more - check them out today!! https://huggingface.co/nvidia/OpenCodeReasoning-Nemotron-32B @Machine_learn

24 522

Introducing Continuous Thought Machines 📚 Paper @Machine_learn

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Llama-Nemotron: Efficient Reasoning Models 📚 Paper @Machine_learn

24 522

Repost from Papers

با عرض سلام از اين مقاله نفرات ٤ و ٥ باقي مونده دوستاني كه مايل به همكاري هستن لطفا با بنده در ارتباط باشن. یکی از ابزارهای خوبی که بنده تونستم توسعه بدم ابزار Stock Ai می باشد. در این ابزار از ۳۶۰ اندیکاتور استفاده کردم. گزارشات back test این ابزار در ویدیو های زیر موجود می باشد. May 2024 : https://youtu.be/aSS99lynMFQ?si=QSk8VVKhLqO_2Qi3 July 2014: https://youtu.be/ThyZ0mZwsGk?si=FKPK7Hkz-mRx-752&t=209 @Raminmousa

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⛽ VoRA: Vision as LoRA ⛽ #ByteDance introduces #VoRA (Vision as #LoRA) — a novel framework that transforms #LLMs into Multimodal Large Language Models (MLLMs) by integrating vision-specific LoRA layers. All training data, source code, and model weights are openly available! Key Resources: Overview: https://t.ly/guNVN Paper: arxiv.org/pdf/2503.20680 GitHub Repo: github.com/Hon-Wong/VoRA Project Page: georgeluimmortal.github.io/vora-homepage.github.io @Machine_learn

24 522

Data-engineer-handbook This is a repo with links to everything you'd ever want to learn about data engineering Creator: DataExpert-io Stars ⭐️: 24.9k Forked by: 4.9k Github Repo: https://github.com/DataExpert-io/data-engineer-handbook #github ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ @Machine_learn

24 522

با عرض سلام دوره ی خصوصی SYFA را داریم برگذار میکنیم که هدف نحوه اشنایی با فرایند نگارش و چاپ مقالات می باشد. جلسات ۱ ساعته و خصوصی می باشند. که هر هفته به ازای هر شخص ۲ جلسه برگذار خواهد شد. جهت ثبت نام و ست کردن زمان با ایدی بنده در ارتباط باشین. @Raminmousa

24 522

Introduction to Machine Learning Laurent Younes 📚 Book @Machine_learn

24 522

Llama-Nemotron: Efficient Reasoning Models 📚 Paper @Machine_learn

24 522

CPPO: Accelerating the Training of Group Relative Policy Optimization-Based Reasoning Models 28 Mar 2025 · Zhihang Lin, Mingbao Lin, Yuan Xie, Rongrong Ji Paper: https://arxiv.org/pdf/2503.22342v1.pdf Code: https://github.com/lzhxmu/cppo Datasets: GSM8K - MATH @Machine_learn

24 522

Repost from Papers

با عرض سلام برای یکی از مقالاتمون نیازمند نفر اول داریم که co-author مقاله هم باشه. مجله ی ارسالی scientific report natue https://www.nature.com/srep/ می باشد. شرایط واگذاری رو در صورت نیاز می تونین با ایدی بنده ست کنین. @Raminmousa @Machine_learn @Paper4money

24 522

4 advanced attention mechanisms you should know: • Slim attention — 8× less memory, 5× faster generation by storing only K from KV pairs and recomputing V. • XAttention — 13.5× speedup on long sequences via "looking" at the sum of values along diagonal lines in the attention matrix. • Kolmogorov-Arnold Attention, KArAt — Adaptable attention with learnable activation functions using KANs instead of softmax. • Multi-token attention (MTA) — Lets the model consider groups of nearby words together for smarter long-context handling. Read the overview of them in our free article on https://huggingface.co/blog/Kseniase/attentions @Machine_learn

24 522

Crystal Generation with Space Group Informed Transformer 🖥 Github: https://github.com/deepmodeling/crystalformer 📕 Paper: https://arxiv.org/abs/2504.02367v1 🔗 Dataset: https://paperswithcode.com/dataset/alex-20 @Machine_learn

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https://t.me/papercite

24 522

International AI Safety Report 📚 Report @Machine_learn

24 522

Repost from Papers

با عرض سلام می خواهیم مقاله ی جدیدی را تحت عنوان زیر شروع کنیم: Comparative survey on Transfer Learning for multi-modal wound image classification مقالات قبلی که در این رابطه نوشتیم به ترتیب زیر می باشند: تیم 1: [1]چاپ شده در Expert system with application تیم 2:[2] سابمیت شده در Scientific report تیم 3:[3] سابیمت شده در IEEE transaction نفرات 2 تا 5 این مقاله خالی می باشند. این نفرات علاوه بر مرور مقالات و تحلیل نتایج هزینه سرور را نیز متقبل می شوند. [1] Mousa, Ramin, et al. "Multi-modal wound classification using wound image and location by Swin Transformer and Transformer." Expert Systems with Applications (2025): 127077. [2] Mousa, Ramin, et al. "Integrating Vision and Location with Transformers: A Multimodal Deep Learning Framework for Medical Wound Analysis." arXiv preprint arXiv:2504.10452 (2025). [3] Mousa, Ramin, Ehsan Matbooe, and Hakimeh Khojasteh. "Multi-Modal Wound Classification Using Wound Image and Location by Xception and Gaussian Mixture Recurrent Neural Network (GMRNN)." (2025 هزینه نفرات به ترتيب 2:400$ 3:300$ 4:250$ 5:200$ می باشد. ژونال مد نظر Scientific Reprot (Nature) @Raminmousa @Machine_learn @Paper4money

24 522

BioPars: Persian biomedical data Model: BioPars Dataset: ParsMed Benchmark: BioParsQa Next week submit @Machine_learn

24 522

FlowReasoner: Reinforcing Query-Level Meta-Agents 📚 Paper @Machine_learn

24 522

$Signatures of unconventional superconductivity near reentrant and fractional quantum anomalous Hall insulators 📚 Paper @Mach$

Signatures of unconventional superconductivity near reentrant and fractional quantum anomalous Hall insulators 📚 Paper @Machine_learn

24 522

امکان ریکام دادن در این مقاله هستش. دوستانی که می خوان اپلایی کنن بهترین فرصت. @Raminmousa