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Machine learning books and papers
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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 24 506 subscribers, ranking 8 028 in the Education category and 13 775 in the Iran region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 506 subscribers.
According to the latest data from 02 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -109 over the last 30 days and by 5 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 6.29%. Within the first 24 hours after publication, content typically collects 2.04% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 541 views. Within the first day, a publication typically gains 500 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 03 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.
24 506
Subscribers
+524 hours
-147 days
-10930 days
Posts Archive
🔸لیستی از کانالهای فعال در حوزههای هوشمصنوعی، علم داده و یادگیری ماشین و برنامه نویسی
هوش مصنوعی:
1⃣ @ai_python
2⃣ @HomeAI
3⃣ @Ai_Tv
4⃣ @ailib
علم داده:
1⃣ @DataAnalysis
2⃣ @DataPlusScience
تحلیل داده و تصمیمگیری دادهمحور:
1⃣ @Mr_IE
2⃣ @sbubusiness
یادگیری ماشین:
1⃣ @Machine_learn
برنامه نویسی و مهندسی کامپیوتر:
1⃣ @pythony
2⃣ @Programming4all_0to100
@Machine_learn
MaxUp: A Simple Way to Improve Generalization of Neural Network Training
A new approach to augmentation both images and text. The idea is to generate a set of augmented data with some random perturbations or transforms and minimize the maximum, or worst case loss over the augmented data. By doing so, the authors implicitly introduce a smoothness or robustness regularization against the random perturbations, and hence improve the generation performance. Testing MaxUp on a range of tasks, including image classification, language modeling, and adversarial certification, it is consistently outperforming the existing best baseline methods, without introducing substantial computational overhead.
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paper: https://arxiv.org/abs/2002.09024
#augmentations #SOTA #ml
🔸لیستی از کانالهای فعال در حوزههای هوشمصنوعی، علم داده و یادگیری ماشین و برنامه نویسی
هوش مصنوعی:
1⃣ @ai_python
2⃣ @HomeAI
3⃣ @Ai_Tv
4⃣ @ailib
علم داده:
1⃣ @DataAnalysis
2⃣ @DataPlusScience
تحلیل داده و تصمیمگیری دادهمحور:
1⃣ @Mr_IE
2⃣ @sbubusiness
یادگیری ماشین:
1⃣ @Machine_learn
برنامه نویسی و مهندسی کامپیوتر:
1⃣ @pythony
2⃣ @Programming4all_0to100
🔸لیستی از کانالهای فعال در حوزه هوش مصنوعی،علم داده و یادگیری ماشین
هوش مصنوعی:
1⃣ @ai_python
2⃣ @HomeAI
3⃣ @Ai_Tv
4⃣ @ailib
علم داده و یادگیری ماشین :
1⃣ @Programming4all_0to100
2⃣ @Machine_learn
3⃣ @nemoudar
4⃣ @sbubusiness
5⃣ @DataPlusScience
"Deep learning for Computer Vision by Jason brownlee"
Please share it with me
@raminmousa
https://machinelearningmastery.com/deep-learning-for-computer-vision/
Jason Brownlee
Machine Learning Mastery With Python
#book #python
@Machine_learn
@machine_learn
A Survey on The Expressive Power of Graph Neural Networks
This is the best survey on the theory on GNNs I'm aware of. It produces so many illustrative examples on what GNN can and cannot distinguish.
It's funny, it's made by Ryoma Sato who I already saw from other works on GNNs and I thought it's one of these old Japanese professors with long beard and strict habits, but it turned out to be a 1st year MSc student 🇯🇵
Generative Adversarial Networks with python by Jason Brownlee #book and #code @Machine_learn
1.Generative Adversarial Networks with python by Jason Brownlee
2.imbalanced classification with python by Jason Brownlee
I want these two books
@Raminmousa
Fresh picks from ArXiv
This week is accepted papers to CVPR and WebConf, submissions to ICML, 130-page survey on knowledge graphs and algorithms for rainbow vertex coloring 🌈
@Machine_learn
CVPR 20
* Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud
* Bundle Adjustment on a Graph Processor
@Machine_learn
WebConf 20
* Just SLaQ When You Approximate: Accurate Spectral Distances for Web-Scale Graphs
* Heterogeneous Graph Transformer
* Learning to Hash with Graph Neural Networks for Recommender Systems
@Machine_learn
ICML 20
* Neural Enhanced Belief Propagation on Factor Graphs by group of Max Welling
@Machine_learn
Survey
* A Survey on The Expressive Power of Graph Neural Networks
@Machine_learn
by Ryoma Sato
* A Survey on Deep Hashing Methods
* Knowledge Graphs
* Knowledge Graphs and Knowledge Networks: The Story in Brief
@Machine_learn
Graph Theory
* Properties of Erdős-Rényi Graphs
* Algorithms for the rainbow vertex coloring problem on graph classes
* Direct Product Primality Testing of Graphs is GI-hard
Announcing TensorFlow Quantum: An Open Source Library for Quantum Machine Learning
@Machine_learn
https://ai.googleblog.com/2020/03/announcing-tensorflow-quantum-open.html
@Machine_learn
#XGBoost
XGBoost: An Intuitive Explanation
Ashutosh Nayak :
https://towardsdatascience.com/xgboost-an-intuitive-explanation-88eb32a48eff
Artificial Intelligence Forecasting of Covid-19 in China
#paper
#Corona_virus
@Machine_learn
@Machine_learn
MARKOV CHAIN MONTE CARLO (MCMC) SAMPLING
https://www.tweag.io/posts/2019-10-25-mcmc-intro1.html
Habr ru: https://habr.com/ru/company/piter/blog/491268/
سلام دوستان برای یه کار تحقیق نیاز به یسری دیتاست در زمینه تحلیل احساس فارسی داریم (به غیر از توییتر) ممنون میشم اگر کسی داره در پیوی برای بنده به اشتراک بزاره
@raminmousa
@Machine_learn
More than 200 NLP datasets - this is gold (last update 21.01.202)
https://quantumstat.com/dataset/dataset.html
and also Google provided dataset search tool for publicly available datasets:
https://datasetsearch.research.google.com/
