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.
显示更多📈 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 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 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 天
+23930 天
帖子存档
11 016
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/
11 016
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
11 016
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
11 016
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
