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 24 499 subscribers, ranking 8 053 in the Education category and 13 774 in the Iran region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 499 subscribers.

According to the latest data from 30 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -131 over the last 30 days and by -4 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 7.24%. Within the first 24 hours after publication, content typically collects 1.98% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 773 views. Within the first day, a publication typically gains 484 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 01 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.

24 499

Subscribers

-424 hours

-187 days

-13130 days

1 773

Post views

~ 48424 hours

~ 59648 hours

7.24%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

24 499

The fashion industry is on the verge of an unprecedented change. The implementation of machine learning, computer vision, and artificial intelligence (AI) in fashion applications is opening lots of new opportunities for this industry. This paper provides a comprehensive survey on this matter, categorizing more than 580 related articles into 22 well-defined fashion-related tasks. Such structured task-based multi-label classification of fashion research articles provides researchers with explicit research directions and facilitates their access to the related studies, improving the visibility of studies simultaneously. For each task, a time chart is provided to analyze the progress through the years. Furthermore, we provide a list of 86 public fashion datasets accompanied by a list of suggested applications and additional information for each. link: https://arxiv.org/abs/2111.00905 @Machine_learn

24 499

🔊 Torchaudio: an audio library for PyTorch Github: https://github.com/pytorch/audio Paper: https://arxiv.org/abs/2110.15018v1 Dataset: https://paperswithcode.com/dataset/ljspeech @Machine_learn

24 499

تفخیف 50% برای دوستان عزیز با زمان محدود. جهت خرید به ایدی بنده مراجعه کنین @Raminmousa

24 499

GoEmotions: A Dataset for Fine-Grained Emotion Classification http://ai.googleblog.com/2021/10/goemotions-dataset-for-fine-grained.html @Machine_learn

24 499

#RNN #Slide and #Survey @Machine_learn

24 499

Recurrent Neural Networks for Edge Intelligence: A Survey #Survey #RNN @Machine_learn

24 499

Survey on Recurrent Neural Network in Natural Language Processing #Survey #RNN @Machine_learn

24 499

Time Series Data Imputation: A Survey on Deep Learning Approaches #RNN #Survey @Machine_learn

24 499

A Critical Review of Recurrent Neural Networks for Sequence Learning #Survey #RNN @Machine_learn

24 499

Recurrent Neural Network TINGWU WANG, MACHINE LEARNING GROUP, UNIVERSITY OF TORONTO #Slide #RNN @Machine_learn

24 499

Computational Tutorial: An introduction to LSTMs in Tensorflow #Slide #RNN @Machine_learn

24 499

Introduction to RNNs! Arun Mallya! #RNN #Slide @Machine_learn

24 499

AlphaRotate: A Rotation Detection Benchmark using TensorFlow Github: https://github.com/yangxue0827/RotationDetection Paper: https://arxiv.org/abs/2109.11906v1 Dataset: https://paperswithcode.com/dataset/dota Documentation: https://rotationdetection.readthedocs.io/en/latest/ @Machine_learn

24 499

https://t.me/kdnuggets

24 499

Unified Data Collection for Visual-Inertial Calibration via Deep Reinforcement Learning Github: https://github.com/ethz-asl/Learn-to-Calibrate Paper: https://arxiv.org/abs/2109.14974v1 @Machine_learn

24 499

TensorFlow Model Optimization Toolkit — Collaborative Optimization API https://blog.tensorflow.org/2021/10/Collaborative-Optimizations.html @Machine_learn

24 499

تخفیف ۵۰٪ پکیچ تا پایان امشب @Raminmousa

24 499

Transfer Learning for Natural Language Processing #Book @Machine_learn

24 499

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

24 499

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