Available now! Telegram Research 2025 — the year's key insights 
Machine learning books and papers
Open in Telegram
Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
Show more📈 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 24 519 subscribers, ranking 8 070 in the Education category and 13 778 in the Iran region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 519 subscribers.
According to the latest data from 23 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -162 over the last 30 days and by -13 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 8.28%. 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 2 031 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 4.
- 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 24 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.
24 519
Subscribers
-1324 hours
-547 days
-16230 days
Posts Archive
WILDCHAT-50M: A Deep Dive Into the Role of Synthetic Data in Post-Training
🖥 Github: https://github.com/penfever/wildchat-50m
📕 Paper: https://arxiv.org/abs/2501.18511v1
🧠 Dataset: https://huggingface.co/collections/nyu-dice-lab/wildchat-50m-679a5df2c5967db8ab341ab7
@Machine_learn
📃Can social network analysis contribute to supply chain
management? A systematic literature review and
bibliometric analysis
📎 Study paper
@Machine_learn
⁉️چرا وقتی میتونی با همین دانش در ایران درآمد دلاری داشته باشی، به ریال قانع میشی؟
💎 با شرکت در وبینار «دکتر علیرضا قیمتی» از تجربههای این کارآفرین جوان در بازارهای بینالمللی رایگان استفاده کن.
⭕️ سرفصلهای مهم این وبینار:
- معرفی پلتفرمهای جهانی فریلنسری
- شرایط حضور در بازارهای جهانی
- میزان مهارت و میانگین حقوق دریافتی
✅ این وبینار مناسب چه رشتههایی هست؟
- برنامهنویسی، طراحی سایت، UI & UX دیزاین، معماری، مهندسی مکانیک، موشن گرافیک، دیتا ساینس و... .
- دانشجویان و افراد باتجربه در رشتههای فوق
و تمام افرادی که با یک لپتاپ قابلیت ارائه مهارت خود را دارند.
⛔️فرصت استثنایی⛔️
⚠️ آفر ویژه این هفتهمون مخصوص افرادی هست که در وبینار شرکت میکنن، پس این هفته رو از دست نده!
📌 لینک ثبتنام مستقیم رایگان :
https://links.etekanesh.com/Machine_le
⬅️ تلگرام :
@TekaneshAcademy
👥 پشتیبانی :
@Academy_Tekanesh
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
Paper: https://arxiv.org/pdf/2401.02954v1.pdf
Code: https://github.com/deepseek-ai/deepseek-llm
Dataset: AlignBench
@Machine_learn
اخرین زمان برای مشارکت در این پروژه تا اخر شب...!
@Raminmousa
🐋 DeepClaude
git clone https://github.com/getasterisk/deepclaude.git
cd deepclaude
▪ Github
▪Docs
@Machine_learnRepost from Papers
با عرض سلام در يكي از پروژه هاي طبقه بندي سرطان پوست نياز به مشاركت داريم. جايگاه نفر سوم خالي مي باشد.
🔸🔻🔸🔻🔸🔻🔻
@Raminmousa
⭐️ Fast Think-on-Graph: Wider, Deeper and Faster Reasoning of Large Language Model on Knowledge Graph
🖥 Github: https://github.com/dosonleung/fasttog
📕 Paper: https://arxiv.org/abs/2501.14300v1
@Machine_learn
How to run 🐋 DeepSeek locally on your Computer
@Machine_learn
نفر ٧ از اين پروژه باقي مونده و تا اخر امشب مي تونيم اضافه كنيم به تيم...!
@Raminmousa
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Paper submitted by #DeepSeek team has generated significant attention in the AI community.
This work addresses the enhancement of reasoning capabilities in Large Language Models (LLMs) through the application of reinforcement learning techniques. The authors introduce a novel framework, DeepSeek-R1, which aims to improve LLM reasoning abilities by incorporating incentives for logical reasoning processes within their training. This integration of reinforcement learning allows LLMs to go beyond basic linguistic processing, developing sophisticated reasoning methods that can boost performance across a wide array of complex applications.
This approach has cause lots of discussions in different communities, but it definitely opens up the whole new direction of development for the research.
Paper: https://arxiv.org/abs/2501.12948
#nn #LLM
@Machine_learn
JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and Generation
We present JanusFlow, a powerful framework that unifies image understanding and generation in a single model. JanusFlow introduces a minimalist architecture that integrates autoregressive language models with rectified flow, a state-of-the-art method in generative modeling. Our key finding demonstrates that rectified flow can be straightforwardly trained within the large language model framework, eliminating the need for complex architectural modifications. To further improve the performance of our unified model, we adopt two key strategies: (i) decoupling the understanding and generation encoders, and (ii) aligning their representations during unified training. Extensive experiments show that JanusFlow achieves comparable or superior performance to specialized models in their respective domains, while significantly outperforming existing unified approaches across standard benchmarks. This work represents a step toward more efficient and versatile vision-language models.
Paper: https://arxiv.org/pdf/2411.07975v1.pdf
Code: https://github.com/deepseek-ai/janus
Datasets: GQA MMBench MM-Vet SEED-Bench
@Machine_learn
📃 Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development
📎 Study the paper
@Machine_learn
Repost from Papers
در پروژه MedicalRec ما نياز به يه نفر جهت مشاركت داريم(جايگاه ٧)
Project Title: MedRec: Medical recommender system for image classification without retraining
Github: https://github.com/Ramin1Mousa/MedicalRec
Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence
Impact factor: 20.8
🔸 7- 200$❌
جهت مشارکت می تونید به ایدی بنده پیام بدین.
اموزش نحوه ی انجام کار ، ریویی مقاله و کد نویسی هم داخل این کار خواهیم داشت.
🧠🧠🧠🧠🧠
@Raminmousa
