Machine learning books and papers

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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn

📈 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

1 541

Post views

~ 50024 hours

~ 61348 hours

6.29%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

24 506

🔸لیستی از کانال‌های فعال در حوزه‌های هوش‌مصنوعی، علم داده , پایتون و یادگیری ماشین هوش مصنوعی: 1️⃣ @Ai_Tv 2⃣ @HomeAi علم داده: 1️⃣ @DataAnalysis تحلیل داده و تصمیم‌گیری داده‌محور: 1️⃣ @Mr_IE یادگیری ماشین و یادگیری عمیق : 1️⃣ @Machine_learn 2⃣ @cvision آموزش پایتون و برنامه نویسی : 1⃣ @pythonchallenge 2⃣ @raspberry_python 3⃣ @Koolac_Org 4⃣ @Programming4all_0to100

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@Machine_learn NeRF: Neural Radiance Fields http://www.matthewtancik.com/nerf Tensorflow implementation: https://github.com/bmild/nerf Paper: https://arxiv.org/abs/2003.08934v1

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@Machine_learn The TensorFlow tutorials are written as Jupyter notebooks and run directly in Google Colab—a hosted notebook environment that requires no setup. FROM BEGINNERS TO EXPERTS * Source Codes * Videos * Libraries and extensions https://www.tensorflow.org/tutorials

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@Machine_learn In a chord diagram (or radial network), entities are arranged radially as segments with their relationships visualised by arcs that connect them. The size of the segments illustrates the numerical proportions, whilst the size of the arc illustrates the significance of the relationships1. Chord diagrams are useful when trying to convey relationships between different entities, and they can be beautiful and eye-catching. https://github.com/shahinrostami/chord #python

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@Machine_learn Local-Global Video-Text Interactions for Temporal Grounding Github: https://github.com/JonghwanMun/LGI4temporalgrounding Paper: https://arxiv.org/abs/2004.07514

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@Machine_learn Machine Learning and Data Science free online courses to do in quarantine A. Beginner courses 1. Machine Learning 2. Machine Learning with Python B. Intermediate courses 3. Neural Networks and Deep Learning 4. Convolutional Neural Networks C. Advanced course 5. Advanced Machine Learning Specialization

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@Machine_learn Regularizing Meta-Learning via Gradient Dropout Code: https://github.com/hytseng0509/DropGrad Paper: https://arxiv.org/abs/2004.05859

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@Machine_learn PUBG Data Analysis https://youtu.be/sah4m-9Il7o

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@Machine_learn Hidden Markov Model - Implemented from scratch https://zerowithdot.com/hidden-markov-model/

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@Machine_learn Python Machine Learning Published by: John Wiley & Sons, Inc.

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@Machine_learn Free course Deep Unsupervised Learning https://sites.google.com/view/berkeley-cs294-158-sp20/home

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@Machine_learn TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrieval Github: https://github.com/jayleicn/TVRetrieval PyTorch implementation : https://github.com/jayleicn/TVCaption Paper: https://arxiv.org/abs/2001.09099v1

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Python Data Visualization Cookbook Second Edition @Machine_learn

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@Machine_learn Deep unfolding network for image super-resolution Deep unfolding network inherits the flexibility of model-based methods to super-resolve blurry, noisy images for different scale factors via a single model, while maintaining the advantages of learning-based methods. Github: https://github.com/cszn/USRNet Paper: https://arxiv.org/pdf/2003.10428.pdf

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⚠️⚠️ANNOUNCEMENT⚠️⚠️ Learn machine Learning,AI,Data Science & more https://t.me/joinchat/AAAAAFJoS2BjRz1pa-hdwQ Learn music instruments https://t.me/joinchat/AAAAAFJoS2BjRz1pa-hdwQ Learn Video Editing, photo Editing https://t.me/joinchat/AAAAAFJoS2BjRz1pa-hdwQ Learn Programming Language https://t.me/joinchat/AAAAAFJoS2BjRz1pa-hdwQ Learn Python https://t.me/joinchat/AAAAAFJoS2BjRz1pa-hdwQ Learn Ethical Hacking https://t.me/joinchat/AAAAAFJoS2BjRz1pa-hdwQ Learn More & Develop your Mind https://t.me/joinchat/AAAAAFJoS2BjRz1pa-hdwQ

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@Machine_learn Advancing Self-Supervised and Semi-Supervised Learning with SimCLR https://ai.googleblog.com/2020/04/advancing-self-supervised-and-semi.html Code and Pretrained-Models: https://github.com/google-research/simclr Papare: https://arxiv.org/abs/2002.05709

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artificial_vision_language_processing_robotics@NetworkArtificial.pdf5.57 MB

24 506

🔸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 یادگیری ماشین و یادگیری عمیق : 1️⃣ @Machine_learn 2⃣ @cvision هوش تجاری و پایگاه داده: 1⃣ @BIMining 2⃣ @sql_server آموزش پایتون و برنامه نویسی : 1⃣ @pythonchallenge 2⃣ @raspberry_python 3⃣ @Programming4all_0to100

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Artificial Vision and Language Processing for Robotics #vision #languageprocessing #python @Machine_learn

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! pip install covid ‌ 🦠 @Machine_learn