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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
Repost from Github LLMs
Welcome to Ollama's Prompt Engineering Interactive Tutorial
🔗 Github
https://t.me/deep_learning_proj
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
⭐️ Region-Aware Text-to-Image Generation via Hard Binding and Soft Refinement
RAG-Diffusion now supports FLUX.1 Redux!
🔥 Ready to take control? Customize your region-based images with our training-free solution and achieve powerful, precise results!
🔗 Code: https://github.com/NJU-PCALab/RAG-Diffusion
@Machine_learn
Super beginner-friendly book on Linear Algebra
🔗 Book
@Machine_learn
Repost from Github LLMs
LLM-based agents for Software Engineering
"Large Language Model-Based Agents for Software Engineering: A Survey".
https://github.com/FudanSELab/Agent4SE-Paper-List.
https://t.me/deep_learning_proj
Repost from Papers
با عرض سلام نفر سوم و چهارم 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 نفرات به ترتیب ۲۰ و ۱۵ تومن می باشد. این هزینه صرف تسویه سرورها خواهد شد.
@Raminmousa
@Machine_learn
https://t.me/+SP9l58Ta_zZmYmY0
Repost from Papers
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
@Raminmousa
@Machine_learn
https://t.me/+SP9l58Ta_zZmYmY0
📚 Deep Learning with Python Develop Deep Learning Models on Theano and TensorFLow Using Keras by Jason Brownlee
🔗 Book
@Machine_learn
📃Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects
📎 Study the paper
@Machine_learn
Repost from Papers
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
@Raminmousa
@Machine_learn
https://t.me/+SP9l58Ta_zZmYmY0
Gaussian Processes for Machine Learning
📚 link
@Machine_learn
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
