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 24 502 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 24 502 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。

24 502

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

24 502

AdaBelief Optimizer Project page: https://juntang-zhuang.github.io/adabelief/ Paper: https://arxiv.org/abs/2010.07468 GitHub: https://github.com/juntang-zhuang/Adabelief-Optimizer @Machine_learn

24 502

An Empirical Analysis of Visual Features for Multiple Object Tracking in Urban Scenes Github: https://github.com/Guepardow/Visual-features Paper: https://arxiv.org/abs/2010.07881 @Machine_learn

24 502

Recreating Historical Streetscapes Using Deep Learning and Crowdsourcing http://ai.googleblog.com/2020/10/recreating-historical-streetscapes.html @Machine_learn

24 502

Partial FC Distributed deep learning training framework for face recognition. Github: https://github.com/deepinsight/insightface/tree/master/recognition/partial_fc Paper: https://arxiv.org/abs/2010.05222v1 Largest Face Recognition Dataset: https://www.dropbox.com/sh/gdix4jabzlwtk72/AAAXEItN1zwdo_tzOx5-QqHWa?dl=0 @Machine_learn

24 502

Transforming sounds into musical instruments used in a variety of styles, from Baroque to jazz using machine learning, created by the Magenta and AIUX team within Google Research. https://sites.research.google/tonetransfer Intro Video: https://youtu.be/bXBliLjImio Blog post: https://magenta.tensorflow.org/ddsp Colab: https://colab.research.google.com/github/magenta/ddsp/blob/master/ddsp/colab/demos/timbre_transfer.ipynb https://github.com/magenta/ddsp/tree/master/ddsp/colab/tutorials Github: https://github.com/magenta/ddsp @Machine_learn

24 502

1) bit.ly/fund_dataProc 2) bit.ly/2XY4Suw 3) leaps.analyttica.com/courses/overview/Fundamentals-of-Data-Analytics?auth_modal=true&register=true&utm_source=Ins_PraFDA&utm_ 4) bit.ly/31W6yH1 5) https://bit.ly/2FHfrM1 @Machine_learn

24 502

This is a list of awesome articles about object detection. If you want to read the paper according to time https://github.com/amusi/awesome-object-detection 👉@Machine_learn

24 502

Cool New Features in Python 3.9 https://realpython.com/courses/cool-new-features-python-39/ @Machine_learn

24 502

#Datamining @Machine_learn

24 502

Real-time semantic segmentation in the browser - Made With TensorFlow.js https://www.youtube.com/watch?v=3XzQQlh_p1c 🆔@Machine_learn

24 502

Boosting quantum computer hardware performance with TensorFlow https://blog.tensorflow.org/2020/10/boosting-quantum-computer-hardware.html @Machine_learn

24 502

👁 S E E I N G T H E O R Y #book @Machine_learn

24 502

Seeing Theory 🎲 A visual introduction to probability and statistics https://seeing-theory.brown.edu/index.html#4thPage 📗 Free book: https://seeing-theory.brown.edu/doc/seeing-theory.pdf @Machine_learn

24 502

http://www.cs.toronto.edu/~jungao/def-grid/ @Machine_learn

24 502

🔸لیستی از برترین کانال‌های آموزشی در زمینه های هوش‌مصنوعی, پایتون و یادگیری ماشین ‏❯ هوش مصنوعی: 1️⃣ @Ai_Tv 2⃣ @HomeAI ‏❯ یادگیری ماشین و یادگیری عمیق : 1️⃣ @Machine_learn ‏❯ علم داده 1⃣ @DataAnalysis 2⃣ @mr_ie ‏❯ تنسورفلو و پایگاه داده : 1⃣ @cvision 2⃣ @SQL_SERVER ‏❯ آموزش پایتون و برنامه نویسی : 1⃣ @pythonchallenge 2⃣ @Raspberry_Python 3⃣ @Programming4all_0to100

24 502

Measuring dataset similarity using optimal transport https://www.microsoft.com/en-us/research/blog/measuring-dataset-similarity-using-optimal-transport/ @Machine_learn

24 502

Towards Fast, Accurate and Stable 3D Dense Face Alignment Releases the pre-trained first-stage pytorch models of MobileNet-V1 structure, the pre-processed training&testing dataset and codebase. Github: https://github.com/cleardusk/3DDFA Paper: https://arxiv.org/abs/2009.09960v1 @Machine_learn

24 502

Link : bit.ly/3hKhoVn @Machine_learn

24 502

Pixelopolis, a self-driving car demo from Google I/O built with TF-Lite @Machine_learn https://blog.tensorflow.org/2020/07/pixelopolis-self-driving-car-demo-tensorflow-lite.html

24 502

Improving Sparse Training with RigL https://ai.googleblog.com/2020/09/improving-sparse-training-with-rigl.html Github: https://github.com/google-research/rigl Paper: https://arxiv.org/abs/1911.11134 @Machine_learn