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AI & Deep Learning

AI & Deep Learning

前往频道在 Telegram

All about Deep Learning, LLMs #deeplearning #deep_learning #AI #ML Follow for quality content amid all the noise in #AI.

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📈 Telegram 频道 AI & Deep Learning 的分析概览

频道 AI & Deep Learning (@deeplearning005) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 11 015 名订阅者,在 技术与应用 类别中位列第 10 976,并在 印度 地区排名第 35 734

📊 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 11 015 名订阅者。

根据 25 八月, 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 239,过去 24 小时变化为 0,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 12.83%。内容发布后 24 小时内通常能获得 2.45% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 1 413 次浏览,首日通常累积 270 次浏览。
  • 互动与反馈: 受众积极参与,单帖平均反应数为 6
  • 主题关注点: 内容集中在 developer, openai 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:
All about Deep Learning, LLMs #deeplearning #deep_learning #AI #ML Follow for quality content amid all the noise in #AI.

凭借高频更新(最新数据采集于 26 八月, 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。

11 015
订阅者
无数据24 小时
+477
+23930
帖子存档
Great News! MCP will now be stateless, this was a much awaited change. Read full blog: https://blog.modelcontextprotocol.io/posts/2026-07-28-release-candidate/

Grok 4.6 achieves frontier intelligence across several agentic coding and knowledge work benchmarks. It matches GPT-5.6 Sol on the Artificial Analysis Intelligence Index, which is a composite score of nine benchmarks. https://x.ai/news/grok-4-6

TabFM (Tabular Foundation Model) is a pretrained tabular foundation model developed by Google Research for tabular data regression and classification. At inference time, TabFM does not require training parameters on your dataset; instead, it leverages in-context learning by reading your training data as "context" to make instant predictions on new test samples. https://github.com/google-research/tabfm

TabFM (Tabular Foundation Model) is a pretrained tabular foundation model developed by Google Research for tabular data regression and classification. At inference time, TabFM does not require training parameters on your dataset; instead, it leverages in-context learning by reading your training data as "context" to make instant predictions on new test samples. https://github.com/google-research/tabfm