Available now! Telegram Research 2025 — the year's key insights 
Machine learning books and papers
Open in Telegram
Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
Show more📈 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
Posts Archive
با عرض سلام دوستانی که می خواهند در مقاله ی بالا شرکت کنند می تونن به ایدی بنده جهت اسم نویسی پیام بدن نفر اول 500$ و نفر دوم 400$ جهت مشارکت. با تشکر
@Raminmousa
Sentiment analysis (SA) is a computational analysis of ideas, feelings and opinions, and uses natural language processing techniques, computational techniques and text analyses to extract polarity (positive, negative or neutral) from non-structured documents or textual comments. the purpose of the multi-domain SA is that the classifier training is based on a set of labelled data in a way that reduces the need for large amounts of data on specific domains and to address the challenges of data scarcity in them with the help of existing data on other domains. The purpose of this paper is to present a new method for analysing the Persian multi-domain SA using DL approaches. The proposed Bi-GRUCapsule approach uses the combination of two networks Bi-GRU and CapsuleNet to solve the multi-domain SA problem that Bi-GRU has the role of extracting features for CapsuleNet. the proposed approach was evaluated using the Digikala dataset and has received acceptable accuracy compared to the existing approaches.
Algorithms for Clustering Data #Book #Clustering @Machine_learn
DATA CLUSTERING Algorithms and Applications #Clustering #Book @Machine_learn
Cluster Analysis: Basic Concept #Clustering #book @Machine_learn
📑 Extreme Zero-Shot Learning for Extreme Text Classification
Github: https://github.com/amzn/pecos
Paper: https://arxiv.org/abs/2112.08652v1
@Machine_learn
📑 Extreme Zero-Shot Learning for Extreme Text Classification
Github: https://github.com/amzn/pecos
Paper: https://arxiv.org/abs/2112.08652v1
@Machine_learn
Training Machine Learning Models More Efficiently with Dataset Distillation
http://ai.googleblog.com/2021/12/training-machine-learning-models-more.html
@Machine_learn
با عرض سلام دوستانی که نیاز به تهیه ی پکیچ ما دارند می تونن به ایدی بنده پیام بدن @Raminmousa . همچنین دوستانی که نیاز به مشاوره در رابطه با ابده های جدید ، کارهای عملی، پروپوزال و پایان نامه دارند می تونن با ایدی بنده یا شماره واتس اپ بنده 09333900804 در ارتباط باشند.
🕷 Bayesian Active Learning (BaaL)
Github: https://github.com/ElementAI/baal
Documentation: https://baal.readthedocs.io.
Paper: https://arxiv.org/abs/2112.06586v1
Blog: https://www.elementai.com/news/2019/element-ai-makes-its-bayesian-active-learning-library-open-source
@Machine_learn
🎓 GAN-Supervised Dense Visual Alignment
Github: https://github.com/wpeebles/gangealing
Project: https://www.wpeebles.com/gangealing
Paper: https://arxiv.org/abs/2112.04894v1
Dataset: https://paperswithcode.com/dataset/celeba
@Machine_learn
Movie Name Generation Using GPT-2
https://www.nbshare.io/notebook/976197999/Movie-Name-Generation-Using-GPT-2/
@Machine_learn
General and Scalable Parallelization for Neural Networks
http://ai.googleblog.com/2021/12/general-and-scalable-parallelization.html
@Machine_learn
Improving Vision Transformer Efficiency and Accuracy by Learning to Tokenize
http://ai.googleblog.com/2021/12/improving-vision-transformer-efficiency.html
@Machine_learn
A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents
Github: https://github.com/extreme-classification/deepxml
Paper: https://arxiv.org/abs/2111.06685v1
Dataset: https://paperswithcode.com/dataset/extreme-classification
@Machine_learn
🔹 LUMINOUS: Indoor Scene Generation for Embodied AI Challenges
Github: https://github.com/amazon-research/indoor-scene-generation-eai
Paper: https://arxiv.org/abs/2111.05527v1
Dataset: https://paperswithcode.com/dataset/ai2-thor
@Machine_learn
Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review
Github: https://github.com/Ildaron/Laser_control
Paper: https://www.mdpi.com/2072-4292/13/21/4486
@Machine_learn
Deep Learning for disentangling Liquidity-constrained and Strategic Default #DL #Liquidity #Paper @Machine_learn
