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

📊 Audience metrics and dynamics

Since its creation on невідомо, 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, 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 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
QuantLib Python Cookbook — L. Ballabio, G. Balaraman (en) 2017. #middle #book @Machine_learn
QuantLib Python Cookbook — L. Ballabio, G. Balaraman (en) 2017. #middle #book @Machine_learn

♦️آموزش Python از 0 تا 100 🔹 هوش مصنوعی 🔸 تست نفوذ 🔹هک و امنیت 🔸ترفندهای ناب 🔹سورس کد 🐍 @PythonForever شدید توصیه میشه�
♦️آموزش Python از 0 تا 100 🔹 هوش مصنوعی 🔸 تست نفوذ 🔹هک و امنیت 🔸ترفندهای ناب 🔹سورس کد 🐍 @PythonForever شدید توصیه میشه👌

اقای میثم عسگری فقدان پدر بزرگوارتان ما را سخت  اندوهگین ساخت غفران و رحمت الهی  برای آن عزیز از دست رفته و سلامتی و طول عمر با عزت برای جناب عالی از پروردگار  متعال خواهانیم

How to CombineNeural Networks andDecision Trees #Book #beginner @Machine_learn

Easy Python Programming for Beginners — Felix Alvaro (en) 2015 #Python #Book #beginner @Machine_learn

Easy Python Programming for Beginners — Felix Alvaro (en) 2015 #Python #Book #beginner @Machine_learn
Easy Python Programming for Beginners — Felix Alvaro (en) 2015 #Python #Book #beginner @Machine_learn

How to Calculate Precision, Recall, F1, and More for Deep Learning Models #precision #Recall #F1 #Metrics @Machine_learn https://machinelearningmastery.com/how-to-calculate-precision-recall-f1-and-more-for-deep-learning-models/

Tensorflow World Resources — Amirsina Torfi (en) 2019 #beginner #book @Machine_learn

Tensorflow World Resources — Amirsina Torfi (en) 2019 #beginner #book @Machine_learn
Tensorflow World Resources — Amirsina Torfi (en) 2019 #beginner #book @Machine_learn

Introduction to Tensorflow 2.0 | Tensorflow 2.0 Features and Changes #video @Machine_learn https://www.youtube.com/watch?v=3O-5DuqKaRo

TOP PROGRAMMING LANGUAGES for a DATA SCIENTIST #guide @Machine_learn

TOP PROGRAMMING LANGUAGES for a DATA SCIENTIST #guide @Machine_learn
TOP PROGRAMMING LANGUAGES for a DATA SCIENTIST #guide @Machine_learn

Python For Data Science Cheat Sheet Keras #Python #Cheat_sheet @Machine_learn

Python For Data Science Cheat Sheet Scikit-Learn #Python #Cheat_sheet @Machine_learn

discriminative : 1:#Regression 2:#Logistic regression 3:#decision tree(Hunt) 4:#neural network(traditional network, deep netw
discriminative : 1:#Regression 2:#Logistic regression 3:#decision tree(Hunt) 4:#neural network(traditional network, deep network) 5:#Support Vector Machine(SVM) Generative: 1:#Hidden Markov model 2:#Naive bayes 3:#K-nearest neighbor(KNN) 4:#Generative adversarial networks(GANs) Deep learning: 1:CNN 2:RNN 3:LSTM 4:CapsuleNet 5:Siamese: siamese cnn siamese lstm siamese bi-lstm siamese CapsuleNet 6:time series data جهت درخواست و راهنمایی در رابطه با پیاده سازی مقالات و پایان نامه ها در رابطه با مباحث deep learning و machine learning با ایدی زیر در ارتباط باشید @Raminmousa

#Reinforcement Learning — A. Nandy, M. Biswas (en) 2018 #book #middle #python @Machine_learn

#Reinforcement Learning — A. Nandy, M. Biswas (en) 2018 #book #middle #python @Machine_learn
#Reinforcement Learning — A. Nandy, M. Biswas (en) 2018 #book #middle #python @Machine_learn

Machine learning method for state preparation and gate synthesis on photonic #quantum_computers #Paper @Machine_learn

Machine learning method for state preparation and gate synthesis on photonic #quantum_computers #Paper @Machine_learn