现已上线！2025 年 Telegram 研究 — 年度关键洞察 
Machine learning books and papers
前往频道在 Telegram
📈 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 8 036，并在 伊朗 地区排名第 13 785 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 01 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 -127，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 7.47%。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 1 829 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 02 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
-207 天
-12730 天
帖子存档
📈 کارگاه Kernel Methods for Pattern Analysis
📅 تاریخ برگزاری: پنج شنبه ۲۷ آذر از ساعت ۱۳ الی ۱۶.۳۰ و جمعه ۲۸ آذر ۱۰ الی ۱۶.۳۰
🖥 این دوره به صورت آنلاین برگزار خواهد شد.
🔖 ثبت نام زود هنگام این دوره را از دست ندهید!
⭕️ برای مشاهده جزئیات بیشتر و ثبت نام روی این لینک کلیک کنید.
❌ دوره دارای ظرفیت محدود می باشد.
🕙 مدت این دوره: 8 ساعت
➖➖➖➖➖➖➖➖
@LoopAcademy
Lipton, A., & de Prado, M. L. (2020). A closed-form solution for optimal mean-reverting trading strategies.
#paper
@Machine_learn
AN INTRODUCTION TO ALGORITHMIC TRADING
Basic to Advanced Strategies
#book
@Machine_learn
Weight Agnostic Neural Networks
link: https://weightagnostic.github.io/
@Machine_learn
Implementation of Adam and AMSGrad Optimizers
Project: https://lab-ml.com/labml_nn/optimizers/adam.html
AMSGrad: https://lab-ml.com/labml_nn/optimizers/amsgrad.html
GitHub: https://github.com/lab-ml/nn
Full list: https://lab-ml.com/labml_nn/index.html
👌
@Machine_learn
pixelNeRF: Neural Radiance Fields from One or Few Images.
Github: https://github.com/sxyu/pixel-nerf
Paper: http://arxiv.org/abs/2012.02190
@Machine_learn
Fully Convolutional Networks for Panoptic Segmentation.
Github: https://github.com/yanwei-li/PanopticFCN
Paper: https://arxiv.org/pdf/2012.00720.pdf
@Machine_learn
Using AutoML for Time Series Forecasting
http://ai.googleblog.com/2020/12/using-automl-for-time-series-forecasting.html
@Machine_learn
This channel has launced to introduce books and educational videos in the field of artificial intelligence.
Animating Pictures with Eulerian Motion Fields
New method for single image animation. Authors promised to release code soon!
Website: https://eulerian.cs.washington.edu
Paper: https://eulerian.cs.washington.edu/animating_pictures_2020.pdf
ArXiV: https://arxiv.org/abs/2011.15128
YouTube: https://www.youtube.com/watch?v=4zKliOMilGY
#DL #animation #WashingtonUni
@Machine_learn
Using AutoML for Time Series Forecasting
http://ai.googleblog.com/2020/12/using-automl-for-time-series-forecasting.html
@Machine_learn
Text Extraction from a Table Image, using PyTesseract and OpenCV
https://levelup.gitconnected.com/text-extraction-from-a-table-image-using-pytesseract-and-opencv-3342870691ae
@Machine_learn
Tutorial on Generative Adversarial Networks (GANs) with Keras and TensorFlow
Nice tutorial with enough theory to understand what you are doing and code to get it done.
Link: https://www.pyimagesearch.com/2020/11/16/gans-with-keras-and-tensorflow/
#Keras #TensorFlow #tutorial #wheretostart #GAN
@Machine_learn
Free Course Advanced Computer Vision with TensorFlow
https://www.coursera.org/learn/advanced-computer-vision-with-tensorflow
@Machine_learn
Learning Efficient GANs using Differentiable Masks and Co-Attention Distillation
Github: https://github.com/SJLeo/DMAD
Paper: https://arxiv.org/abs/2011.08382
@Machine_learn
Using GANs to Create Fantastical Creatures
http://ai.googleblog.com/2020/11/using-gans-to-create-fantastical.html
@Machine_learn
Edge AI
Convergence of Edge Computing and Artificial Intelligence
#book #AI
@Machine_learn
Introduction to Deep Learning Using R
A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R
#book #DL
@Machine_learn
