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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 24 508 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 24 508 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.

24 508
Subscribers
+324 hours
-97 days
-10130 days
Posts Archive
#Deep Reinforcement Learning in TensorFlow #slide @Machine_learn

#Introduction to Reinforcement Learning and Policy-Gradients with Tensor-Flow #slide @Machine_learn

#Reinforcement Learning: A Tutorial #paper @Machine_learn

#Deep Reinforcement Learning: Q-Learning #slide @Machine_learn

#Tutorial: Deep Reinforcement Learning #slide @Machine_learn

#Deep learning with TensorFlow #book @Machine_learn

#Getting start with TensorFlow #book @Machine_learn

#Classification and regression trees #paper @Machine_learn

#Introduction To Machine Learning #lecture0 #author:@RaminMousa @Machine_learn

#10machine learning algorithm #book #Machine_learn

TensorFlow for Deep Learning #2018 #Linear Regression --> Reinforcement Learning @Machine_learn

#Machine Learning Yearning #Andrew Ng #book @Machine_learn

#Procedural Content Generation via Machine Learning (PCGML) #paper @Machine_learn

#Deep learning with python #book #Machine_learn

#logistic regression #simple code #spam detection @Machine_learn #author:@RaminMousa

#An Encounter with Google's TensorFlow (Revised) #tutorial @Machine_learn

#Basics_of_Linear_Algebra_for Machine Learning #book @Machine_learn

#Nick_McClure_Tensorflow_machine #book @Machine_learn

#learning_scikit_learn_machine_learning #book @Machine_learn

با عرض سلام دوستانی که نیاز به پیاده سازی و یا یادگیری مطالب زیر دارند با ایدی ادمین در ارتباط باشند. به زودی نمونه کد به همراه توضیح کامل از مباحث زیر رو داخل گیت هاب قرار میدیم. ✅مباحث متن کاوی: 1:sentiment analysis تحلیل احساسات 2:aspect base sentiment analysis تحلیل احساسات از نقطه نظر ویژگی های شئ 3:part of speech(pos) ایجاد پارسر 4:NER تشخیص نهاده های اسمی 5:text classification طبقه بندی متن. (فارسی ، انگلیسی) ✅شبکه های عصبی عمیق: 1:CNN(Text,Image) 2:RNN(Text,Image) 3:LSTM(Text,Image) 4:CapsuleNet ✅پزشکی: 1:Motif detection 2: community detection 3:ppi networks 4:Grn network 5:Fractal 6:chaos theory ✅داده کاوی: 1:Svm 2:decision tree 3:regression 4:logistic regression 5:KNN,KD_tree 6:naive bayes 7:HMM 8:Case base 9:k_means,GMM 10:Fuzzy membership functions . . . ___ @RaminMousa