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Machine learning books and papers
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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 8 048 en la categoría Educación y el puesto 13 749 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 25 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -164, y en las últimas 24 horas de -1, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 7.13%. 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 1 748 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 26 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
-124 horas
-407 días
-16430 días
Archivo de publicaciones
The Arcade Learning Environment (ALE) is a simple framework that allows researchers and hobbyists to develop AI agents for Atari 2600 game
🖥 Github: https://github.com/farama-foundation/arcade-learning-environment
📕 Paper: https://arxiv.org/abs/2410.23810v1
⚡️ Dataset: https://paperswithcode.com/dataset/mujoco
@Machine_learn
📃A Comprehensive Review of Propagation Models in Complex Networks: From Deterministic to Deep Learning Approaches
📎 Study paper
🔺@Machine_learn
📃A Comprehensive Survey on Automatic Knowledge Graph Construction
📎 Study paper
🔺@Machine_learn
💠Title:BERTCaps: BERT Capsule for persian Multi-domain Sentiment Analysis.
🔺Abstract:
Sentiment classification is widely known as a domain-dependent problem. In order to learn an accurate domain-specific sentiment classifier, a large number of labeled samples are needed, which are expensive and time-consuming to annotate. Multi-domain sentiment analysis based on multi-task learning can leverage labeled samples in each single domain, which can alleviate the need for large amount of labeled data in all domains. In this article, the purpose is BERTCaps to provide a multi-domain classifier. In this model, BERT was used for Instance Representation and Capsule was used for instance learning. In the evaluation dataset, the model was able to achieve an accuracy of 0.9712 in polarity classification and an accuracy of 0.8509 in domain classification.
journal: https://www.sciencedirect.com/journal/array
If:2.3
جايگاه ٤ اين مقاله رو نياز داريم. فردا زمان سابميت هستش.
دوستاني كه مايل به شركت هستن مي تونن به ايدي بنده پيام بدن.
@Raminmousa
@Paper4money
@Machine_learn
🔸برترین کانالهای آموزشی در زمینه های هوشمصنوعی, پایتون و یادگیری ماشین
☑️ هوش مصنوعی :
1️⃣ @Ai_Tv
2⃣ @ai_in_research
3⃣ @eventai
☑️معرفی و آموزش کار با هوشمصنوعی های مولد و پرامپت نویسی
1⃣ @Ai_NewsTv
☑️ علم داده :
1️⃣ @DataPlusScience
☑️ یادگیری ماشین :
1️⃣ @Machine_learn
☑️ آموزش پایتون و یادگیری ماشین:
1⃣ @Python4all_pro
2⃣ @raspberry_python
3⃣ @pythony
☑️ دوره های رایگان و منابع آموزشی پایتون ، علم داده و یادگیری ماشین :
1⃣ @programmers_street
20 Python Libraries You Aren't Using But Should
📕 Book
@Machine_learn
Repost from Github LLMs
🖥 Awesome LLM Strawberry (OpenAI o1)
▪ Github
✅https://t.me/deep_learning_proj
📖 A Data-Centric Introduction to Computing
link
@Machine_learn
Financial Statement Analysis with Large Language Models (LLMs)
📕 Book
@Machine_learn
Foundations Of The Theory Of Probability by
Andrey Nikolaevich Kolmogorov
🔥🔥🔥
Read the book
@Machine_learn
Repost from Papers
با عرض سلام مقاله زیر در مرحله ی اولیه ارسال می باشد. نفرات 2و ۳ خالی می باشد. دوستانی که نیاز دارند می تونن به ایدی بنده پیام بدن. همچنین امکان ریکامدادن بعد اتمام کار وجود داره.
💠💠
Title:
Automated Concrete Crack Detection and Geometry Measurement Using YOLOv8
Description:
This paper presents a comprehensive approach for automatic detection and quantification of concrete cracks using the YOLOv8 deep learning model. By leveraging advanced object detection capabilities, our system identifies concrete cracks in real-time with high accuracy, addressing challenges of complex backgrounds and varying crack patterns. Following crack detection, we employ image processing techniques to measure key geometric parameters such as width, length, and area. This integrated system enables rapid, precise analysis of structural integrity, offering a scalable solution for infrastructure monitoring and maintenance.
🔸Target Journal:
Nature, Scientific Reports
@Raminmousa
@Machine_learn
https://t.me/+SP9l58Ta_zZmYmY0
Repost from Papers
با عرض سلام مقاله زیر در مرحله ی اولیه ارسال می باشد. نفرات ۱ تا ۳ جایگاه ها خالی می باشد. دوستانی که نیاز دارند می تونن به ایدی بنده پیام بدن.
💠💠
Title:
Automated Concrete Crack Detection and Geometry Measurement Using YOLOv8
Description:
This paper presents a comprehensive approach for automatic detection and quantification of concrete cracks using the YOLOv8 deep learning model. By leveraging advanced object detection capabilities, our system identifies concrete cracks in real-time with high accuracy, addressing challenges of complex backgrounds and varying crack patterns. Following crack detection, we employ image processing techniques to measure key geometric parameters such as width, length, and area. This integrated system enables rapid, precise analysis of structural integrity, offering a scalable solution for infrastructure monitoring and maintenance.
Target Journal:
Nature, Scientific Reports
@Raminmousa
@Machine_learn
https://t.me/+SP9l58Ta_zZmYmY0
This repository contains a collection of resources in the form of eBooks related to Data Science, Machine Learning, and similar topics.
📖 book
💠@Machine_learn
🔸برترین کانالهای آموزشی در زمینه های هوشمصنوعی, پایتون و یادگیری ماشین
❯ هوش مصنوعی:
1️⃣ @Ai_Tv
2⃣ @HomeAI
3⃣ @ai_in_research
4⃣ @eventai
5⃣ @Ai_NewsTv
❯ علم داده :
1️⃣ @DataPlusScience
❯ یادگیری ماشین :
1️⃣ @Machine_learn
❯ آموزش پایتون و یادگیری ماشین:
1⃣ @Python4all_pro
❯ منابع و کتابهای پایتون ، علم داده و یادگیری ماشین :
1⃣ @programmers_street
📃 Plant-based anti-cancer drug discovery using computational approaches
📎 Study the paper
@Machine_learn
