Machine learning books and papers

前往频道在 Telegram

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

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📈 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

1 541

帖子浏览量

~ 50024 小时

~ 61348 小时

6.29%

参与率

无数据

每日帖子数

Ads index

beta

帖子存档

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

24 506

@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

24 506

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

24 506

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

24 506

@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

24 506

@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

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🔸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