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Machine learning books and papers

Machine learning books and papers

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πŸ“ˆ 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 24 509 subscribers, ranking 8 019 in the Education category and 13 748 in the Iran region.

πŸ“Š Audience metrics and dynamics

Since its creation on Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 24 509 subscribers.

According to the latest data from 04 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -101 over the last 30 days and by 3 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 6.50%. Within the first 24 hours after publication, content typically collects 2.21% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 1 594 views. Within the first day, a publication typically gains 541 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
  • Thematic interests: Content is focused on key topics such as disorder, psy, Ω…Ω‚Ψ§Ω„Ω‡, framework, graph.

πŸ“ Description and content policy

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

Thanks to the high frequency of updates (latest data received on 05 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.

24 509
Subscribers
+324 hours
-97 days
-10130 days
Posts Archive
#lan_Goodfellow,_Yos...a_bengio,_Aaron #book #Machine_learn

Real-World Machine Learning β€” Henrik Brink, Joseph W. Richards, Mark Fetherolf (en) 2017 #book #theory @Machine_learn

Real-World Machine Learning β€” Henrik Brink, Joseph W. Richards, Mark Fetherolf (en) 2017 #book #theory @Machine_learn
Real-World Machine Learning β€” Henrik Brink, Joseph W. Richards, Mark Fetherolf (en) 2017 #book #theory @Machine_learn

#Introduction to Deep Learning β€” Sandro Skansi (en) 2018 #book #beginner @Machine_lean

#Introduction to Deep Learning β€” Sandro Skansi (en) 2018 #book #beginner @Machine_lean
#Introduction to Deep Learning β€” Sandro Skansi (en) 2018 #book #beginner @Machine_lean

#practical Web Scraping for Data Science #book #Machine_learn

#intruduction to Deep Learning #book #Machine_learn

#Data Mining for Business Analytics #book #Machine_learn

TensorFlow Machine Learning Cookbook β€” Nick McClure (en) 2017 . #book #python @Machine_learn

TensorFlow Machine Learning Cookbook β€” Nick McClure (en) 2017 . #book #python @Machine_learn
TensorFlow Machine Learning Cookbook β€” Nick McClure (en) 2017 . #book #python @Machine_learn

Thoughtful Machine Learning with Python – Matthew Kirk (en) 2016 #book #theory @Machine_learn

Thoughtful Machine Learning with Python – Matthew Kirk (en) 2016 #book #theory @Machine_learn
Thoughtful Machine Learning with Python – Matthew Kirk (en) 2016 #book #theory @Machine_learn

Smart Grid using Big Data Analytics – R. C. Qiu, P. Antonik (en) 2017 #book @Machine_learn

Smart Grid using Big Data Analytics – R. C. Qiu, P. Antonik (en) 2017 #book @Machine_learn
Smart Grid using Big Data Analytics – R. C. Qiu, P. Antonik (en) 2017 #book @Machine_learn

Join us: AI + Python + Deep Learning = @ai_python Latest Articles = @ai_python_arXiv

Big Data, Data Mining, and Machine Learning – Jared Dean (en) 2014 #book #theory @Machine_learn

Big Data, Data Mining, and Machine Learning – Jared Dean (en) 2014 #book #theory @Machine_learn
Big Data, Data Mining, and Machine Learning – Jared Dean (en) 2014 #book #theory @Machine_learn

Practical Machine Learning – Sunila Gollapudi (en) #book #theory @Machine_learn

Practical Machine Learning – Sunila Gollapudi (en) #book #theory @Machine_learn
Practical Machine Learning – Sunila Gollapudi (en) #book #theory @Machine_learn

Bayesian Reasoning and Machine Learning β€” D. Barber (en) 2012/2017. #book #beginner #theory @Machine_learn