متاح الآن! بحث تيليغرام 2025 — أهم رؤى العام 
Machine learning books and papers
الذهاب إلى القناة على Telegram
Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
إظهار المزيد📈 نظرة تحليلية على قناة تيليجرام Machine learning books and papers
تُعد قناة Machine learning books and papers (@machine_learn) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 24 504 مشتركاً، محتلاً المرتبة 8 031 في فئة التعليم والمرتبة 13 740 في منطقة إيران.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 24 504 مشتركاً.
بحسب آخر البيانات بتاريخ 29 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار -131، وفي آخر 24 ساعة بمقدار -1، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 7.01%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 1.97% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 1 718 مشاهدة. وخلال اليوم الأول يجمع عادةً 484 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 1.
- الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل disorder, psy, مقاله, framework, graph.
📝 الوصف وسياسة المحتوى
يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“Admin: @Raminmousa
ID: @Machine_learn
link: https://t.me/Machine_learn”
بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 30 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التعليم.
24 504
المشتركون
-124 ساعات
-277 أيام
-13130 أيام
أرشيف المشاركات
fmri alzheimer's disease classification
target journal:https://www.sciencedirect.com/journal/computerized-medical-imaging-and-graphics
نفر ٣ رو كم داريم.
نيازمند كسي هستيم كه بتونه هزينه سرور رو پرداخت كنه .
@Raminmousa
@Machine_learn
https://t.me/+SP9l58Ta_zZmYmY0
⚡️ Biggest open text dataset release of the year: SmolTalk is a 1M sample big synthetic dataset that was used to train SmolLM v2.
TL;DR;
🧩 New datasets: Smol-Magpie-Ultra (400K) for instruction tuning; Smol-contraints (36K) for precise output; Smol-rewrite (50K) & Smol-summarize (100K) for rewriting and summarization.
🤝 Public Dataset Integrations: OpenHermes2.5 (100K), MetaMathQA & NuminaMath-CoT, Self-Oss-Starcoder2-Instruct, LongAlign & SystemChats2.0
🥇 Outperforms the new Orca-AgenInstruct 1M when trained with 1.7B and 7B models
🏆 Outperform models trained on OpenHermes and Magpie Pro on IFEval and MT-Bench
distilabel to generate all new synthetic datasets
🤗 Released under Apache 2.0 on huggingface
Apache 2.0
Synthetic generation pipelines and training code released.
Dataset: https://huggingface.co/datasets/HuggingFaceTB/smoltalk
Generation Code: https://github.com/huggingface/smollm
Training Code: https://github.com/huggingface/alignment-handbook/tree/main/recipes/smollm2
@Machine_learn
Repost from Papers
Title: Transformer and XGBoost for time-series forecasting of Bitcoin prices using high-dimensional features
ABSTRACT: Bitcoin price prediction based on price indicators has become a hot field of study. In this article, Bitcoin price prediction is discussed based on hash rate features. For this purpose, a series of price indices were used in the beginning and the selection of features was done among 20 features. On the other hand, the selection of features was also done on the raw data of eight rates. This research used forecasting for one, seven, thirty and ninety days. In the classification based on raw features, the highest accuracy is 81%, and for a 90-day interval, on the other hand, the lowest RMSE value is 1.85, which is for a one-day interval. In the classification based on the features extracted from the indicators, the highest accuracy is 73% for the 90-day interval and the lowest RMSE is 1.58 for the 1-day interval.
نياز به co-author براي اين مقاله هستيم شرايط رو اگر كسي از دوستان داشت به بنده مراجعه كنن.
@Raminmousa
@Machine_learn
https://t.me/+SP9l58Ta_zZmYmY0
ShowUI is a lightweight vision-language-action model for GUI agents.
🖥 Github: https://github.com/showlab/showui
📕 Paper: https://arxiv.org/abs/2411.17465v1
🌟 Dataset: https://huggingface.co/datasets/showlab/ShowUI-desktop-8K
@Machine_learn
📖 Penn State University's "Graph Theory"
📌 Lectures
@Machine_learn
O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson?
🖥 Github: https://github.com/gair-nlp/o1-journey
📕 Paper: https://arxiv.org/abs/2411.16489v1
🌟 Dataset: https://paperswithcode.com/dataset/lima
💠@Machine_learn
Repost from Papers
Title: Transformer and XGBoost for time-series forecasting of Bitcoin prices using high-dimensional features
ABSTRACT: Bitcoin price prediction based on price indicators has become a hot field of study. In this article, Bitcoin price prediction is discussed based on hash rate features. For this purpose, a series of price indices were used in the beginning and the selection of features was done among 20 features. On the other hand, the selection of features was also done on the raw data of eight rates. This research used forecasting for one, seven, thirty and ninety days. In the classification based on raw features, the highest accuracy is 81%, and for a 90-day interval, on the other hand, the lowest RMSE value is 1.85, which is for a one-day interval. In the classification based on the features extracted from the indicators, the highest accuracy is 73% for the 90-day interval and the lowest RMSE is 1.58 for the 1-day interval.
نياز به co-author براي اين مقاله هستيم شرايط رو اگر كسي از دوستان داشت به بنده مراجعه كنن.
@Raminmousa
@Machine_learn
https://t.me/+SP9l58Ta_zZmYmY0
👩💻 Julia Programming Language for Biologists
📎 Study the paper
@Machine_learn
تيم دوم :
fmri alzheimer's disease classification
target journal:https://www.sciencedirect.com/journal/computerized-medical-imaging-and-graphics
نفر ٣ رو كم داريم.
نيازمند كسي هستيم كه بتونه هزينه سرور رو پرداخت كنه و توي نگارش مقاله كمكمون كنه.
@Raminmousa
@Machine_learn
https://t.me/+SP9l58Ta_zZmYmY0
📄Advancing biomolecular simulation through exascale HPC, AI and quantum computing
📎 Study the paper
@Machine_learn
C O M P U T E R V I S I O N : F O U N D AT I O N S A N D A P P L I C AT I O N S
🖥 book
@Machine_learn
Primers • Overview of Large Language Models
📖 Link
@Machine_learn
Pattern recognition and machine learning
📖 Link
@Machine_learn
ليست پروژه هاي جديد كه دوستان مي تونن به تيم هاي ما اضافه بشن.
تيم اول:
Survey on whole slide image
target journal: https://www.nature.com/srep/
نفرات ٤ و ٥ رو كم داريم
تيم دوم :
fmri alzheimer's disease classification
target journal:https://www.sciencedirect.com/journal/computerized-medical-imaging-and-graphics
نفر ٣ رو كم داريم.
@Raminmousa
@Machine_learn
https://t.me/+SP9l58Ta_zZmYmY0
Repost from Papers
با عرض سلام مقالات اين ماه سابميت شده با كمك دوستان
1- Skin cancer detection
Group 1:
-Ramin M(Zanjan University), Saeed C(Tehran), Mohammad.M,*,+, Seyyed Mohammad.O(Sharif),Parsa.H(Sharif), and Soroush.S(
Raderon AI Lab, BC, Canada)
submit: https://www.nature.com/srep/
Group2:
Ramin Mousa(Zanjan),Amir Ali. B(University of Tehran), Hakimeh. K( University of Zanjan)
submit: https://www.sciencedirect.com/journal/computerized-medical-imaging-and-graphics
2- Survey:
Survey on evaluation of metrics for learning system
Ramin Mousa, Masoud.p
submit: https://www.sciencedirect.com/journal/knowledge-based-systems
3- NLP
Group1: multi-domain SA
BertCapsule:
Mohammadali M, Soghra M, Amir.P, Mehrshad.E and Ramin Mousa
submit: https://www.sciencedirect.com/journal/array
به زودي ليستي از كارهاي جديد معرفي ميشه كه در صورت نياز دوستان مي تونن به گروه هامون اضافه بشن.
@Raminmousa
@Machine_learn
https://t.me/+SP9l58Ta_zZmYmY0
