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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
@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/
