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
@Machine_learn
Gradient Centralization: A New Optimization Technique for Deep Neural Networks
Code: https://github.com/Yonghongwei/Gradient-Centralization
Paper: https://arxiv.org/abs/2004.01461
@Machine_learn
Flows for simultaneous manifold learning and density estimation
A new class of generative models that simultaneously learn the data manifold as well as a tractable probability density on that manifold.
Code: https://github.com/johannbrehmer/manifold-flow
Paper: https://arxiv.org/abs/2003.13913
@Machine_learn
Train transformer language models with reinforcement learning.
https://lvwerra.github.io/trl/
Code: https://github.com/openai/lm-human-preferences
Paper: https://arxiv.org/pdf/1909.08593.pdf
🔸لیستی از کانالهای فعال در حوزههای هوشمصنوعی، علم داده , پایتون و یادگیری ماشین
هوش مصنوعی:
1️⃣ @Ai_Tv
2️⃣ @AI_PYTHON
3️⃣ @HomeAi
علم داده:
1️⃣ @DataAnalysis
تحلیل داده و تصمیمگیری دادهمحور:
1️⃣ @Mr_IE
2️⃣ @python4finance
یادگیری ماشین:
1️⃣ @Machine_learn
آموزش پایتون و برنامه نویسی :
1️⃣ @pythony
2️⃣ @pythonchallenge
3️⃣ @raspberry_python
4️⃣ @Programming4all_0to100
@Machine_learn
Graph Isomorphism Software
Open-source software for finding isomorphism or canonical forms of graphs.
* Nauty/Traces
* Bliss
* saucy
* conauto
* Gi-ext
New paper by Yandex.MILAB 🎉
Tired of waiting for backprop to project your face into StyleGAN latent space to use some funny vector on it? Just distilate this tranformation by pix2pixHD!
arxiv.org/abs/2003.03581
@Machine_learn
Gradient boost trees with xgboost and scikit-learn #book #python
@Machine_learn
@Machine_learn
An AI program that plays Flappy Bird using reinforcement learning.
Code: https://github.com/taivu1998/FlapAI-Bird
Model: https://stanford-cs221.github.io/autumn2019-extra/posters/18.pdf
Paper: https://arxiv.org/abs/2003.09579
@Machine_learn
Rethinking Image Mixture for Unsupervised Visual Representation Learning
Code: https://github.com/szq0214/Rethinking-Image-Mixture-for-Unsupervised-Learning
Paper: https://arxiv.org/abs/2003.05438v1
@Machine_learn
Graph Machine Learning research groups: Le Song
Le Song (~1981)
- Affiliation: Georgia Institute of Technology;
- Education: Ph.D. at U. of Sydney in 2008 (supervised by Alex Smola);
- h-index: 59;
- Awards: best papers at ICML, NeurIPS, AISTATS;
- Interests: generative and adversarial graph models, social network analysis, diffusion models.
@Machine_learn
Anomaly detection with Keras, TensorFlow, and Deep Learning
In this tutorial, you will learn how to perform anomaly and outlier detection using autoencoders, Keras, and TensorFlow.
https://www.pyimagesearch.com/2020/03/02/anomaly-detection-with-keras-tensorflow-and-deep-learning/
A new paper from Samsung AI Center (Moscow) on unpaired image-to-image translation. Now – without any domain labels, even on training time!
▶️ youtu.be/DALQYKt-GJc
📝 arxiv.org/abs/2003.08791
📉 @Machine_learn
🔸لیستی از کانالهای فعال در حوزههای هوشمصنوعی، علم داده , پایتون و یادگیری ماشین
هوش مصنوعی:
1⃣ @Ai_Tv
2⃣ @AI_PYTHON
3⃣ @HomeAi
4⃣ @ailib
علم داده:
1⃣ @DataAnalysis
2⃣ @BigData_channel
تحلیل داده و تصمیمگیری دادهمحور:
1⃣ @Mr_IE
2⃣ @python4finance
یادگیری ماشین:
1⃣ @Machine_learn
آموزش پایتون و برنامه نویسی :
1⃣ @pythony
2⃣ @pythonchallenge
3⃣ @raspberry_python
4⃣ @Programming4all_0to100
@Machine_learn
Meta-Transfer Learning for Zero-Shot Super-Resolution
Code: https://github.com/JWSoh/MZSR
Paper: https://arxiv.org/abs/2002.12213v1
@Machine_learn
Fast and Easy Infinitely Wide Networks with Neural Tangents
https://ai.googleblog.com/2020/03/fast-and-easy-infinitely-wide-networks.html
Colab notebook: https://colab.research.google.com/github/google/neural-tangents/blob/master/notebooks/neural_tangents_cookbook.ipynb#scrollTo=Lt74vgCVNN2b
Code: https://github.com/google/neural-tangents
Paper: https://arxiv.org/abs/1912.02803
@Machine_learn
Grid Search Optimization Algorithm in Python
https://stackabuse.com/grid-search-optimization-algorithm-in-python/
