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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
Mostrar más📈 Análisis del canal de Telegram Machine learning books and papers
El canal Machine learning books and papers (@machine_learn) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 24 518 suscriptores, ocupando la posición 8 056 en la categoría Educación y el puesto 13 757 en la región Irán.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 24 518 suscriptores.
Según los últimos datos del 24 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -165, y en las últimas 24 horas de -3, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 6.78%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.90% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 663 visualizaciones. En el primer día suele acumular 465 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 1.
- Intereses temáticos: El contenido se centra en temas clave como disorder, psy, مقاله, framework, graph.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Admin: @Raminmousa
ID: @Machine_learn
link: https://t.me/Machine_learn”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 25 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.
24 518
Suscriptores
-324 horas
-477 días
-16530 días
Archivo de publicaciones
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
