Уже доступно! Исследование Telegram 2025 — ключевые инсайты года 
Machine learning books and papers
Открыть в Telegram
Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
Больше📈 Аналитический обзор Telegram-канала Machine learning books and papers
Канал Machine learning books and papers (@machine_learn) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 24 518 подписчиков, занимая 8 056 место в категории Образование и 13 757 место в регионе Иран.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 24 518 подписчиков.
Согласно последним данным от 24 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -165, а за последние 24 часа — -3, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 6.78%. В первые 24 часа после публикации контент обычно набирает 1.90% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 1 663 просмотров. В течение первых суток публикация набирает 465 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 1.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как disorder, psy, مقاله, framework, graph.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“Admin: @Raminmousa
ID: @Machine_learn
link: https://t.me/Machine_learn”
Благодаря высокой частоте обновлений (последние данные получены 25 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Образование.
24 518
Подписчики
-324 часа
-477 дней
-16530 день
Архив постов
Introduction to Data Science – Lecture Material
🔗 Github
@Machine_learn
🀄 GuoFeng Webnovel: A Discourse-Level and Multilingual Corpus of Web Fiction
🖥 Github: https://github.com/longyuewangdcu/guofeng-webnovel
📕 Paper: https://arxiv.org/abs/2412.11732v1
🌟 Dataset: www2.statmt.org/wmt24/literary-trans
@Machine_learn
در اين پروژه ابتدا BioparsData ارائه ميشود كه فرايند جمع اوري سنگيني خواهد داشت. پس از ان BioparsQ ارائه ميشود كه ١٠ هزار سوال بيولوژكي براي ارزيابي مدل ارائه خواهد شد. در انتها Biopars را ارائه خواهيم داد. تمامي اين فرايند پس از نهايي شدن در دسترس عموم قرار ميدهيم.
Repost from Papers
+1
با عرض سلام نفر سوم براي مقاله زير رو خالي داريم.
Title: Alzheimer’s disease (AD) classification
using swin transformer wavelet
and Improved Gray Wolf
Optimization (IGWO)
Abstract: Alzheimer’s disease (AD) is a slow neurological disorder that destroys the thought process, and consciousness, of a human. It directly affects the development of mental ability and neurocognitive functionality. The number of patients with Alzheimer’s disease is increasing day by day, especially in old aged people, who are above 60 years of age, and, gradually, it becomes cause of their death. In this research, our goal is to present ALzSwinTNet for Alzheimer’s classification based on FMRI images. The proposed approach uses wavelet fusion in the swin transformer network to extract features. The igwo and fox optimization approaches were used to find the hyperparameters of the model. ALzSwinTNet was able to achieve an accuracy of 0.98 in 4-class classification and 1 in 2-class classification.
journal: https://www.sciencedirect.com/journal/expert-systems-with-applications
if:7.5
هزینه مشارکت برای نفر سوم ۲۰ تومن می باشد. این هزینه صرف تسویه سرورها خواهد شد.
@Raminmousa
@Machine_learn
https://t.me/+SP9l58Ta_zZmYmY0
📃A Comprehensive Survey on Automatic Knowledge Graph Construction
📎 Study paper
@Machine_learn
PDF Math Translate
DF scientific paper translation with preserved formats
Creator: Byaidu
Stars ⭐️: 5.1k
Forked By: 375
https://github.com/Byaidu/PDFMathTranslate
@Machine_learn
Repost from Papers
+1
با عرض سلام نفر سوم براي مقاله زير رو خالي داريم.
Title: Alzheimer’s disease (AD) classification
using swin transformer wavelet
and Improved Gray Wolf
Optimization (IGWO)
Abstract: Alzheimer’s disease (AD) is a slow neurological disorder that destroys the thought process, and consciousness, of a human. It directly affects the development of mental ability and neurocognitive functionality. The number of patients with Alzheimer’s disease is increasing day by day, especially in old aged people, who are above 60 years of age, and, gradually, it becomes cause of their death. In this research, our goal is to present ALzSwinTNet for Alzheimer’s classification based on FMRI images. The proposed approach uses wavelet fusion in the swin transformer network to extract features. The igwo and fox optimization approaches were used to find the hyperparameters of the model. ALzSwinTNet was able to achieve an accuracy of 0.98 in 4-class classification and 1 in 2-class classification.
journal: https://www.sciencedirect.com/journal/expert-systems-with-applications
if:7.5
هزینه مشارکت برای نفر سوم ۲۰ تومن می باشد. این هزینه صرف تسویه سرورها خواهد شد.
@Raminmousa
@Machine_learn
https://t.me/+SP9l58Ta_zZmYmY0
⚡️ Byte Latent Transformer: Patches Scale Better Than Tokens
Byte Latent Transformer architecture (BLTs), a new byte-level LLM architecture that for the first time, matches tokenization-based LLM performance at scale, with significant improvements in inference efficiency and robustness.
🖥 Github: https://github.com/facebookresearch/blt
📕 Paper: https://arxiv.org/abs/2412.09871v1
🌟 Dataset: https://paperswithcode.com/dataset/mmlu
@Machine_learn
⚡️ Byte Latent Transformer: Patches Scale Better Than Tokens
Byte Latent Transformer architecture (BLTs), a new byte-level LLM architecture that for the first time, matches tokenization-based LLM performance at scale, with significant improvements in inference efficiency and robustness.
🖥 Github: https://github.com/facebookresearch/blt
📕 Paper: https://arxiv.org/abs/2412.09871v1
🌟 Dataset: https://paperswithcode.com/dataset/mmlu
@Machine_learn
Repost from Papers
با عرض سلام در راستاي ادامه تحقيقات مشترك سعي داريم از ١ ام دي ماه روي حوزه ي LLM مدل ها كار كنيم.
این کار تحت نظر استاد
Rex (Zhitao) Ying
انجام میشه.
link: https://scholar.google.com.au/citations?user=6fqNXooAAAAJ&hl=en
۲نفر براي همکاری نياز داريم.
BioPars: a pre-trained biomedical large language model for persian biomedical text mining.
١- مراحل اوليه: جمع اوري متن هاي فارسي بيولوژيكي از منابع (...)
٢- پيش پردازش متن ها و تميز كردن متن ها
٣- اموزش ترنسفورمرها ي مورد نظر
٤- استفاده از بردارها ي اموزش داده شده در سه تسك (...)
دوستاني كه مايل به مشاركت هستن مي تونين تا ١ دي بهم اطلاع بدن.
هزينه سرور به ازاي هر ساعت ١.٢ دلار مي باشد. و حدود ٢ هزار ساعت براي اموزش مدل زباني نياز ميباشد. هزينه به ترتيب براي نفرات علاوه بر انجام تسك ها به صورت زير مي باشد.
🔹نفر چهارم 500 دلار
🔺نفر پنجم 400 دلار
@Raminmousa
@Machine_learn
https://t.me/+SP9l58Ta_zZmYmY0
Large language models (LLMs): survey, technical frameworks,
and future challenges
https://link.springer.com/content/pdf/10.1007/s10462-024-10888-y.pdf
@Machine_learn
Large Language Models: A Survey
https://arxiv.org/pdf/2402.06196
@Machine_learn
WIS Python programming course started in 2024.04
📖 Github
@Machine_learn
Repost from Papers
با عرض سلام نفر ۳ از مقاله زیر رو نیاز داریم.
Title: hybrid deep learnings and machine learning frameworks
for air quality prediction
during the COVID‑19 pandemic
journal: https://www.sciencedirect.com/journal/expert-systems-with-applications
if:7.5
در این مقاله تاثیر ۲۶ مدل ansemble و ترکیبی رو برای پیش بینی کیفیت هوا در بازه ۱ روزه ۳ روزه و ۷ روزه بررسی کردیم. جهت شرکت در این مقاله به ایدی بنده پیام بدین.
@Raminmousa
@Machine_learn
https://t.me/+SP9l58Ta_zZmYmY0
OASIS Alzheimer's Detection
Large-scale brain MRI dataset for deep neural network analysis
About Dataset
The dataset used is the OASIS MRI dataset (https://sites.wustl.edu/oasisbrains/), which consists of 80,000 brain MRI images. The images have been divided into four classes based on Alzheimer's progression. The dataset aims to provide a valuable resource for analyzing and detecting early signs of Alzheimer's disease.
To make the dataset accessible, the original .img and .hdr files were converted into Nifti format (.nii) using FSL (FMRIB Software Library). The converted MRI images of 461 patients have been uploaded to a GitHub repository, which can be accessed in multiple parts.
For the neural network training, 2D images were used as input. The brain images were sliced along the z-axis into 256 pieces, and slices ranging from 100 to 160 were selected from each patient. This approach resulted in a comprehensive dataset for analysis.
Patient classification was performed based on the provided metadata and Clinical Dementia Rating (CDR) values, resulting in four classes: demented, very mild demented, mild demented, and non-demented. These classes enable the detection and study of different stages of Alzheimer's disease progression.
During the dataset preparation, the .nii MRI scans were converted to .jpg files. Although this conversion presented some challenges, the files were successfully processed using appropriate tools. The resulting dataset size is 1.3 GB.
@Machine_learn
