现已上线！2025 年 Telegram 研究 — 年度关键洞察 
Machine learning books and papers
前往频道在 Telegram
📈 Telegram 频道 Machine learning books and papers 的分析概览
频道 Machine learning books and papers (@machine_learn) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 24 506 名订阅者，在 教育 类别中位列第 8 028，并在 伊朗 地区排名第 13 775 位。
📊 受众指标与增长动态
自 невідомо 创建以来，项目保持高速增长，吸引了 24 506 名订阅者。
根据 02 七月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 -109，过去 24 小时变化为 5，整体触达仍然可观。
- 认证状态： 未认证
- 互动率 (ER)： 平均受众互动率为 6.29%。内容发布后 24 小时内通常能获得 2.04% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 1 541 次浏览，首日通常累积 500 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 1。
- 主题关注点： 内容集中在 disorder, psy, مقاله, framework, graph 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台：
“Admin: @Raminmousa
ID: @Machine_learn
link: https://t.me/Machine_learn”
凭借高频更新（最新数据采集于 03 七月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 教育 类别中的关键影响点。
24 506
订阅者
+524 小时
-147 天
-10930 天
帖子存档
🔸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/
