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Machine learning books and papers

Machine learning books and papers

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📈 Analytical overview of Telegram channel Machine learning books and papers

Channel Machine learning books and papers (@machine_learn) in the English language segment is an active participant. Currently, the community unites 24 502 subscribers, ranking 8 028 in the Education category and 13 775 in the Iran region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 502 subscribers.

According to the latest data from 02 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -109 over the last 30 days and by 5 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 6.29%. Within the first 24 hours after publication, content typically collects 2.04% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 1 541 views. Within the first day, a publication typically gains 500 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 1.
  • Thematic interests: Content is focused on key topics such as disorder, psy, مقاله, framework, graph.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn

Thanks to the high frequency of updates (latest data received on 03 July, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.

24 502
Subscribers
+524 hours
-147 days
-10930 days
Posts Archive
Transfer Learning for Natural Language Processing #Book @Machine_learn

Discover the world of Machine Learning using Python algorithm analysis, ide and libraries. Projects focused on beginners. #Book @Machine_learn

Distributed Artificial Intelligence A Modern Approach Edited by Satya Prakash Yadav, Dharmendra Prasad Mahato, and Nguyen Thi Dieu Linh #Book @Machine_learn

Deep Learning with PyTorch Quick Start Guide Learn to train and deploy neural network models in Python David Julian #Book #PyTorch @Machine_learn

با عرض سلام دوستانی که نیاز به تهیه ی پکیچ ما دارند می تونن به ایدی بنده پیام بدن @Raminmousa . همچنین دوستانی که نیاز به مشاوره در رابطه با کارهای عملی، پروپوزال و پایان نامه دارند می تونن با ایدی بنده یا شماره واتس اپ بنده 09333900804 در ارتباط باشند.

Real‑time monitoring of traffic parameters #Paper #2021 @Machine_learn

An improved YOLO-based road traffic monitoring system #Traffic_Monitoring #Paper #2021 @Machine_learn

Road Traffic Condition Monitoring using Deep Learning #Traffic_Monitoring #Paper #2021 @Machine_learn

Traffic Monitoring using an Object Detection Framework with Limited Dataset #Traffic_Monitoring #Paper #2021 @Machine_learn

Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey #Traffic_Monitoring #Paper #2021 @Machine_learn

Artificial Intelligence Enabled Traffic Monitoring System #Traffic_Monitoring #Paper #2021 @Machine_learn

تخفیف 50% دو روزه ی پکیچ، برای تهیه به ایدی بنده پیام بدین @Raminmousa

A novel ensemble deep learning model with dynamic error correction and multi-objective ensemble pruning for time series forecasting #Paper #Ensemble #2021 @Machine_learn

CoBiD-net: a tailored deep learning ensemble model for time series forecasting of covid-19 #Paper #Ensemble #2021 @Machine_learn

Multi-Time Resolution Ensemble LSTMs for Enhanced Feature Extraction in High-Rate Time Series #Paper #Ensemble #2021 @Machine_learn

An Actor-Critic Ensemble Aggregation Model for Time-Series Forecasting #Paper #Ensemble #2021 @Machine_learn

AI in Healthcare: Time-Series Forecasting Using Statistical, Neural, and Ensemble Architectures #Paper #Ensemble #2021 @Machine_learn

DERN: Deep Ensemble Learning Model for Shortand Long-Term Prediction of Baltic Dry Index #Paper #Ensemble #2021 @Machine_learn

Ensemble Deep Learning Models for Forecasting Cryptocurrency Time-Series #Paper #Ensemble #2021 @Machine_learn