Data science/ML/AI
Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist
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网络:📈 Telegram 频道 Data science/ML/AI 的分析概览
频道 Data science/ML/AI (@datascience_bds) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 14 064 名订阅者，在 技术与应用 类别中位列第 9 052，并在 印度 地区排名第 28 197 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 14 064 名订阅者。
根据 25 十月, 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 97，过去 24 小时变化为 4，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 8.86%。内容发布后 24 小时内通常能获得 2.13% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 1 246 次浏览，首日通常累积 300 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 3。
- 主题关注点： 内容集中在 panda, learning, row, api, ethic 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：
“Data science and machine learning hub
Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources.
For beginners, data scientists and ML engineers
👉 https://rebrand.ly/bigdatachannels
DMCA: @disclosure_bds
Contact: @mldatasci...”
凭借高频更新（最新数据采集于 26 十月, 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。
import numpy as np
x = np.array([
[10, 20, 30],
[40, 50, 60]
])
x + 5[[15, 25, 35], [45, 55, 65]]But where did the
5 go? You didn't write a loop. You didn't copy 5 six times.
This is broadcasting.
NumPy automatically treats the smaller object as if it were expanded to match the larger array's shape.
So conceptually:
[[10, 20, 30], [[5, 5, 5], [40, 50, 60]] + [5, 5, 5]]Then it adds them element by element. And it works with arrays too:
x + np.array([1, 2, 3])[[10, 20, 30], [[1, 2, 3], [40, 50, 60]] + [1, 2, 3]]Result:
[[11, 22, 33], [41, 52, 63]]The important part: Broadcasting doesn't literally copy the data into a bigger array. NumPy applies the operation as if the smaller array had been expanded, while avoiding unnecessary copies. That's why this:
x + 5can work even though
x is 2D and 5 is just a scalar.
axis=0 and axis=1 Feel Backwards
You've probably seen:
np.mean(X, axis=0)"Why does axis 0 mean columns?"Think about what NumPy is doing. 📌
axis=0 means:
Collapse the rows.So you move down each column and get one value per column. 📌
axis=1 means:
Collapse the columns.So you move across each row and get one value per row. For:
1 2 3 4 5 6
np.mean(X, axis=0)[2.5, 3.5, 4.5]On the other hand:
np.mean(X, axis=1)[2, 5]Don't memorize the numbers. Just Remember: The axis you specify is the dimension that gets collapsed. #NumPy
