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Machine learning books and papers

Machine learning books and papers

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📈 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 24 502 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 24 502 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.

24 502
Subscribers
+524 hours
-147 days
-10930 days
Posts Archive
LaSOT Large-scale Single Object Tracking (LaSOT) aims to provide a dedicated platform for training data-hungry deep trackers as well as assessing long-term tracking performance. http://vision.cs.stonybrook.edu/~lasot/ Github: https://github.com/HengLan/LaSOT_Evaluation_Toolkit Dataset: http://vision.cs.stonybrook.edu/~lasot/download.html Paper: https://arxiv.org/abs/2009.03465 @Machine_learn

Must Download : CheatSheet Collection For Data Science in ZIP Total Folder - 22 Total Size - 216 MB - Artificial Intelligence - Machine learning - Big Data - OpenCV CheetSheet - Dev Ops - Data Analytics - Python Cheetsheet - Mathematics - Excel - Probability - SQL - Statistics - Deep learning - Data Warehouse - Linux - Interview Question - Docker & Kubernetes - Matlab & R Cheatsheet - Scala CheetSheet @Machine_learn

MushroomRL Reinforcement Learning Python library Github: https://github.com/MushroomRL/mushroom-rl Project page: https://github.com/openai/mujoco-py @Machine_learn

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial 👉👉 Watch Here 👉👉 https://youtu.be/tPYj3fFJGjk ⭐️ About the Author ⭐️ The author of this course is Tim Ruscica, otherwise known as “Tech With Tim” from his educational programming YouTube channel. Tim has a passion for teaching and loves to teach about the world of machine learning and artificial intelligence. Learn more about Tim from the links below: 🔗 YouTube: https://www.youtube.com/channel/UC4JX... 🔗 LinkedIn: https://www.linkedin.com/in/tim-ruscica/ ⭐️ Course Contents ⭐️ ⌨️ Module 1: Machine Learning Fundamentals (00:03:25) ⌨️ Module 2: Introduction to TensorFlow (00:30:08) ⌨️ Module 3: Core Learning Algorithms (01:00:00) ⌨️ Module 4: Neural Networks with TensorFlow (02:45:39) ⌨️ Module 5: Deep Computer Vision - Convolutional Neural Networks (03:43:10) ⌨️ Module 6: Natural Language Processing with RNNs (04:40:44) ⌨️ Module 7: Reinforcement Learning with Q-Learning (06:08:00) ⌨️ Module 8: Conclusion and Next Steps (06:48:24) TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial @Machine_learn

The Little W-Net that Could State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models. Github: https://github.com/agaldran/lwnet Paper: https://arxiv.org/abs/2009.01907v1 @Machine_learn

Neural Networks and Deep Learning A #Textbook @Machine_learn

Machine learning – Linear Regression Course (Free) . Linear regression is perhaps one of the most popular and widely used algorithms in statistics and machine learning. . Link : https://bit.ly/31W6yH1 @Machine_learn

scikit-learn Cookbook Second Edition @Machine_learn

@Machine_learn Axial-DeepLab: Long-Range Modeling in All Layers for Panoptic Segmentation https://ai.googleblog.com/2020/08/axial-deeplab-long-range-modeling-in.html

A Smarter Way to Learn Python: Learn it faster. Remember it longer #book #python @Machine_learn

Free course on Data Visualisation Methods @Machine_learn Link : bit.ly/2XY4Suw

Top 20+ highly ranked Coursera Courses for Data Science & Machine Learning beginners and advanced @Machine_learn https://nuggetsnetwork.com/blog/Top-Coursera-DataScience-Courses.html