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Computer Science and Programming

Computer Science and Programming

前往频道在 Telegram

Channel specialized for advanced topics of: * Artificial intelligence, * Machine Learning, * Deep Learning, * Computer Vision, * Data Science * Python Admin: @otchebuch Memes: @memes_programming Ads: @Source_Ads, https://telega.io/c/computer_science

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📈 Telegram 频道 Computer Science and Programming 的分析概览

频道 Computer Science and Programming (@computer_science_and_programming) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 140 416 名订阅者,在 技术与应用 类别中位列第 811,并在 意大利 地区排名第 88

📊 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 140 416 名订阅者。

根据 01 九月, 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 -772,过去 24 小时变化为 -64,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 8.15%。内容发布后 24 小时内通常能获得 1.97% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 11 442 次浏览,首日通常累积 2 771 次浏览。
  • 互动与反馈: 受众积极参与,单帖平均反应数为 13
  • 主题关注点: 内容集中在 sellerflash, github, developer, pricing, waybienad 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:
Channel specialized for advanced topics of: * Artificial intelligence, * Machine Learning, * Deep Learning, * Computer Vision, * Data Science * Python Admin: @otchebuch Memes: @memes_programming Ads: @Source_Ads, https://telega.io/c/computer_sc...

凭借高频更新(最新数据采集于 02 九月, 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 技术与应用 类别中的关键影响点。

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140 416
订阅者
-6424 小时
-2617 天
-77230 天
帖子存档
Gary Marcus Robust AI 17 February 2020
Gary Marcus Robust AI 17 February 2020

The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence.

Machine Learning for Everyone with great explanation
Machine Learning for Everyone with great explanation

Softmax Splatting for Video Frame Interpolation (using Pytorch)
Softmax Splatting for Video Frame Interpolation (using Pytorch)

Set of free AI, ML, Deep Learning, Reinforcement Learning, Computer Vision, Statistics video lectures collections(last updated 20th February 2020 with 140 items)

CARLA: An Open Urban Driving Simulator Open-source simulator for autonomous driving

PyTorch Implementation and Explanation of Graph Representation Learning papers involving DeepWalk, GCN, GraphSAGE, ChebNet &
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DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills

ZeRO & DeepSpeed: New system optimizations enable training models with over 100 billion parameters
ZeRO & DeepSpeed: New system optimizations enable training models with over 100 billion parameters

Course from MIT 6.S191 "Introduction to Deep Learning". Methods and applications in game play, medicine, language, art, computer vision, robotics and more

This video is synthetic and was created using deep learning