Machine learning books and papers

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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn

📈 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 8 029 in the Education category and 13 742 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 28 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -144 over the last 30 days and by -9 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 6.62%. Within the first 24 hours after publication, content typically collects 1.91% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 623 views. Within the first day, a publication typically gains 468 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 29 June, 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

-924 hours

-317 days

-14430 days

1 623

Post views

~ 46824 hours

~ 57648 hours

6.62%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

24 508

30780512.pdf29.69 MB

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🔸لیستی از برترین کانال‌های آموزشی در زمینه های هوش‌مصنوعی, پایتون و یادگیری ماشین ‏❯ هوش مصنوعی: 1️⃣ @Ai_Tv 2⃣ @ai_python 3⃣ @HomeAI 4⃣ @eventai ‏❯ یادگیری ماشین و یادگیری عمیق : 1️⃣ @Machine_learn ‏❯ تنسورفلو : 1⃣ @cvision ‏❯ علم داده : 1️⃣ @BigDataSchool ‏❯ آموزش پایتون: ‏ 1⃣ @Programming4all_0to100 2⃣ @raspberry_python ❯ منابع یادگیری برنامه‌نویسی : 1️⃣ @pythony

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با عرض سلام نفر دوم این مقاله مونده و امشب سابیمت میشه اگه کسی نیاز داشت به ایدی بنده اعلام کنه. @Raminmousa

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🔸لیستی از برترین کانال‌های آموزشی در زمینه های هوش‌مصنوعی, پایتون و یادگیری ماشین ‏❯ هوش مصنوعی: 1️⃣ @Ai_Tv 2️⃣ @eventai ‏❯ یادگیری ماشین و یادگیری عمیق : 1️⃣ @Machine_learn 2️⃣ @Programming4all_0to100 ‏❯ علم داده : 1️⃣ @BigDataSchool ‏❯ آموزش پایتون: ‏ 1⃣ @raspberry_python ‏

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فقط نفر دوم از اين جايگاه موند ...!

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با عرض سلام مقاله E‏yes estimation and tracking جهت ارکایو شدن تموم شده و نفرات ۲ و ۳ باقی مونده از دوستانی که پردازش تصویر کار میکنن و یا به حوزه دیپ علاقه دارند می تونن در این مقاله شرکت کنند. به زودی نسخه ژورنالش هم اماده میکنیم Abstract E‏yes estimation and tracking are important research issues in computer vision and human-computer interaction. In this paper, a transfer-based learning model is proposed for this purpose. In the proposed approach, the two ResNet50 networks, whose initial weights are taken from ImageNet, are taught in parallel and finally merged into a layer called feature fusion, the output of the two networks. The proposed approach results show that this approach is better than other approaches on the MPIIGaze dataset. The proposed approach achieved an angle error of 5.83, which resulted in a lower error than other approaches. دوستانی که تمایل به شرکت دارند می تونن به ایدی بنده پیام بدن. @Raminmousa

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30340466.pdf5.14 MB

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🔥 DEGramNet: A Novel Convolutional Architecture for Audio Analysis 🚀 📄 Paper: https://link.springer.com/article/10.1007/s00521-023-08849-7 🔥 PyTorch code: https://github.com/robertanto/DEGramNet-torch 📦 TensorFlow code: https://github.com/MiviaLab/DEGramNet 🔗 Google Colab: https://link.springer.com/article/10.1007/s00521-023-08849-7 @Machine_learn

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This channels is for Programmers, Coders, Software Engineers. 0- Python 1- Data Science 2- Machine Learning 3- Data Visualization 4- Artificial Intelligence 5- Data Analysis 6- Statistics 7- Deep Learning 8- programming Languages ✅ Data Science Channels: https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ Main Channel: https://t.me/DataScienceM

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با عرض سلام دو پکیچ یادگیری ماشین و یادگیری عمیق را برای دوستانی که می خواهند تا فرداشب با تخفیف ۵۰٪ مجدد قرار دادیم این تخفیف اخرین سری از تخفیف های این دو پکیچ می باشد 1: introduction to machine learning 2: Regression (linear and non-linear) 3: Tensorflow introduction 4: Tensorflow computaion graph 5: Tensorflow optimizer and loss function 6: Tensorflow linear and non linear regression 7: logistic regression 8: Tensorflow regression ___________ 9: introduction to traditional machine learning *10: knn and desicion tree *11: desicion tree and Naive bayes *12: desicion tree, knn, Naive bayes implementation *13: k-means *14: Guassion Mixture Model(GMM) *15: implementation K-means and GMM _ 16: introduction to Artificial Neural Network 17: Multi-level Neural Network 18: Introduction to Convolution Neural Network 19: Tensorflow Multi-level Neural Network 20:Tensorflow CNN 21:CNN image clasaification 22: Cnn text clasaification 23: Recurrent Neural Network(RNN) جهت تهیه می تونین به ایدی بنده مراجعه کنین @Raminmousa

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Mathematics of Deep Learning.pdf10.81 MB

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