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Machine learning books and papers

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 24 509 名订阅者,在 教育 类别中位列第 8 019,并在 伊朗 地区排名第 13 748

📊 受众指标与增长动态

невідомо 创建以来,项目保持高速增长,吸引了 24 509 名订阅者。

根据 04 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 -101,过去 24 小时变化为 3,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 6.50%。内容发布后 24 小时内通常能获得 2.21% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 1 594 次浏览,首日通常累积 541 次浏览。
  • 互动与反馈: 受众积极参与,单帖平均反应数为 2
  • 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 05 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。

24 509
订阅者
+324 小时
-97
-10130
帖子存档
#Deep learning with keras @Machine_learn

#Big Data Analysis and Deep Learning Applications #book 2019 @Machine_learn

#Matrix Computations 4th #book #Machine_learn

#pattern recognition and machine learning #bishop #book @Machine_learn

#pattern recognition and machine learning #bishop #book @Machine_learn
#pattern recognition and machine learning #bishop #book @Machine_learn

#hands_on machine learning with sciki_learn & TensorFlow #book @Machine_learn

#hands_on machine learning with sciki_learn & TensorFlow #book @Machine_learn
#hands_on machine learning with sciki_learn & TensorFlow #book @Machine_learn

#xgboost_with_python #book @Machine_learn

#Alice_Zheng_Feature_Engineering_for_Machine_Learning_2018 #book @Machine_learn

#Alice_Zheng_Feature_Engineering_for_Machine_Learning_2018 #book @Machine_learn
#Alice_Zheng_Feature_Engineering_for_Machine_Learning_2018 #book @Machine_learn

#Cross-Stitch Networks #Domain adaptation problems #slide #multi-domain SA @Machine_learn

#Transfer Learning with Applications #Sinno Jialin #slide @Machine_learn

#Transfer Learning #Useful slide @Machine_learn

#Transfer Learning Lectures: Introduction #Qiang Yang @Machine_learn

#Multimodal learning from visual and remotely sensed data #thesis @Machine_learn

#ABNORMALITY DETECTION WITH DEEP LEARNING #Master_Thesis @Machine_learn

#Deep Learning Feature Extraction for Image Processing #phd_Thesis @Machine_learn

#Deep Reinforcement Learning to play Space Invaders #paper @Machine_learn