Data Analytics
前往频道在 Telegram
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making. Admin: @HusseinSheikho || @Hussein_Sheikho
显示更多📈 Telegram 频道 Data Analytics 的分析概览
频道 Data Analytics (@dataanalyticsx) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 30 854 名订阅者,在 技术与应用 类别中位列第 4 306,并在 俄罗斯 地区排名第 20 799 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 30 854 名订阅者。
根据 26 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 553,过去 24 小时变化为 11,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 5.22%。内容发布后 24 小时内通常能获得 1.55% 的反应,占订阅者总量。
- 帖子覆盖: 每篇帖子平均可获得 1 610 次浏览,首日通常累积 479 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 sellerflash, buybox, buyer, chaos, effortless 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:
“Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
Admin: @HusseinSheikho || @Hussein_Sheikho”
凭借高频更新(最新数据采集于 28 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。
30 854
订阅者
+1124 小时
+737 天
+55330 天
帖子存档
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Repost from Machine Learning
🔥 Awesome open-source project to learn more about Transformer Models! 🤖✨
We found this interactive website that shows you visually how transformer models work. 🌐📊
Transformer Explainer:
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does this at the level of the data structure and usually works more efficiently for cyclical operations. 🚀
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Do you know that Python can shift sequences without slicing and creating new lists? 🤔
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data = data[-1:] + data[:-1]
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A negative value rotates the queue in the other direction. 🔄python q.rotate(-2)
This is useful for ring buffers, task schedulers, cyclical queues, and round-robin algorithms. ⚙️python workers.rotate(-1)
`
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Repost from Machine Learning with Python
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🚀 𝐑𝐀𝐆 𝐢𝐬 𝐨𝐧𝐥𝐲 𝐚𝐬 𝐠𝐨𝐨𝐝 𝐚𝐬 𝐲𝐨𝐮𝐫 𝐂𝐇𝐔𝐍𝐊𝐈𝐍𝐆 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲
𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 (𝐑𝐀𝐆) 𝐢𝐬 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐡𝐨𝐰 𝐰𝐞 𝐛𝐮𝐢𝐥𝐝 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬—𝐛𝐮𝐭 𝐡𝐞𝐫𝐞’𝐬 𝐭𝐡𝐞 𝐬𝐞𝐜𝐫𝐞𝐭 𝐦𝐨𝐬𝐭 𝐩𝐞𝐨𝐩𝐥𝐞 𝐦𝐢𝐬𝐬:
👉 𝐓𝐡𝐞 𝐰𝐚𝐲 𝐲𝐨𝐮 𝐬𝐩𝐥𝐢𝐭 𝐲𝐨𝐮𝐫 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐬 (𝐜𝐡𝐮𝐧𝐤𝐢𝐧𝐠) 𝐝𝐢𝐫𝐞𝐜𝐭𝐥𝐲 𝐝𝐞𝐭𝐞𝐫𝐦𝐢𝐧𝐞𝐬 𝐡𝐨𝐰 𝐚𝐜𝐜𝐮𝐫𝐚𝐭𝐞, 𝐟𝐚𝐬𝐭, 𝐚𝐧𝐝 𝐬𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐲𝐨𝐮𝐫 𝐑𝐀𝐆 𝐬𝐲𝐬𝐭𝐞𝐦 𝐰𝐢𝐥𝐥 𝐛𝐞.
💡 𝐁𝐚𝐝 𝐜𝐡𝐮𝐧𝐤𝐢𝐧𝐠 = 𝐢𝐫𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐚𝐧𝐬𝐰𝐞𝐫𝐬, 𝐰𝐚𝐬𝐭𝐞𝐝 𝐭𝐨𝐤𝐞𝐧𝐬, 𝐚𝐧𝐝 𝐡𝐢𝐠𝐡𝐞𝐫 𝐜𝐨𝐬𝐭𝐬.
💡 𝐒𝐦𝐚𝐫𝐭 𝐜𝐡𝐮𝐧𝐤𝐢𝐧𝐠 = 𝐩𝐫𝐞𝐜𝐢𝐬𝐞 𝐫𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥, 𝐜𝐨𝐧𝐭𝐞𝐱𝐭-𝐫𝐢𝐜𝐡 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐞𝐬, 𝐚𝐧𝐝 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲.
𝐀𝐟𝐭𝐞𝐫 𝐝𝐞𝐞𝐩 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡, 𝐈 𝐩𝐮𝐭 𝐭𝐨𝐠𝐞𝐭𝐡𝐞𝐫 𝐚 𝐠𝐮𝐢𝐝𝐞 𝐨𝐧 𝐭𝐡𝐞 𝐓𝐎𝐏 𝟏𝟓 𝐂𝐡𝐮𝐧𝐤𝐢𝐧𝐠 𝐌𝐞𝐭𝐡𝐨𝐝𝐬 𝐞𝐯𝐞𝐫𝐲 𝐀𝐈 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐬𝐡𝐨𝐮𝐥𝐝 𝐤𝐧𝐨𝐰:
🔹 𝘍𝘪𝘹𝘦𝘥-𝘚𝘪𝘻𝘦 𝘊𝘩𝘶𝘯𝘬𝘪𝘯𝘨 – 𝘴𝘪𝘮𝘱𝘭𝘦, 𝘱𝘳𝘦𝘥𝘪𝘤𝘵𝘢𝘣𝘭𝘦
🔹 𝘙𝘦𝘤𝘶𝘳𝘴𝘪𝘷𝘦 𝘊𝘩𝘢𝘳𝘢𝘤𝘵𝘦𝘳 𝘚𝘱𝘭𝘪𝘵𝘵𝘪𝘯𝘨 – 𝘧𝘢𝘴𝘵 & 𝘴𝘤𝘢𝘭𝘢𝘣𝘭𝘦
🔹 𝘚𝘦𝘮𝘢𝘯𝘵𝘪𝘤 𝘊𝘩𝘶𝘯𝘬𝘪𝘯𝘨 – 𝘮𝘦𝘢𝘯𝘪𝘯𝘨-𝘣𝘢𝘴𝘦𝘥 𝘱𝘳𝘦𝘤𝘪𝘴𝘪𝘰𝘯
🔹 𝘋𝘰𝘤𝘶𝘮𝘦𝘯𝘵-𝘚𝘱𝘦𝘤𝘪𝘧𝘪𝘤 – 𝘭𝘦𝘷𝘦𝘳𝘢𝘨𝘦 𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦 (𝘗𝘋𝘍𝘴, 𝘏𝘛𝘔𝘓, 𝘔𝘢𝘳𝘬𝘥𝘰𝘸𝘯)
🔹 𝘏𝘪𝘦𝘳𝘢𝘳𝘤𝘩𝘪𝘤𝘢𝘭 – 𝘱𝘢𝘳𝘦𝘯𝘵-𝘤𝘩𝘪𝘭𝘥 𝘳𝘦𝘭𝘢𝘵𝘪𝘰𝘯𝘴𝘩𝘪𝘱𝘴
🔹 𝘚𝘦𝘯𝘵𝘦𝘯𝘤𝘦-𝘈𝘸𝘢𝘳𝘦 – 𝘳𝘦𝘢𝘥𝘢𝘣𝘪𝘭𝘪𝘵𝘺 𝘱𝘳𝘦𝘴𝘦𝘳𝘷𝘦𝘥
🔹 𝘛𝘰𝘬𝘦𝘯-𝘉𝘢𝘴𝘦𝘥 – 𝘢𝘭𝘪𝘨𝘯𝘦𝘥 𝘸𝘪𝘵𝘩 𝘓𝘓𝘔 𝘵𝘰𝘬𝘦𝘯𝘪𝘻𝘢𝘵𝘪𝘰𝘯
🔹 𝘚𝘭𝘪𝘥𝘪𝘯𝘨 𝘞𝘪𝘯𝘥𝘰𝘸 – 𝘰𝘷𝘦𝘳𝘭𝘢𝘱𝘱𝘪𝘯𝘨 𝘤𝘰𝘯𝘵𝘦𝘹𝘵
🔹 𝘛𝘰𝘱𝘪𝘤-𝘉𝘢𝘴𝘦𝘥 – 𝘵𝘩𝘦𝘮𝘢𝘵𝘪𝘤 𝘤𝘭𝘶𝘴𝘵𝘦𝘳𝘪𝘯𝘨
🔹 𝘗𝘳𝘰𝘱𝘰𝘴𝘪𝘵𝘪𝘰𝘯-𝘉𝘢𝘴𝘦𝘥 – 𝘭𝘰𝘨𝘪𝘤𝘢𝘭 𝘶𝘯𝘪𝘵 𝘴𝘱𝘭𝘪𝘵𝘴
🔹 𝘊𝘰𝘯𝘵𝘦𝘹𝘵-𝘈𝘸𝘢𝘳𝘦 – 𝘕𝘓𝘗-𝘥𝘳𝘪𝘷𝘦𝘯 𝘥𝘦𝘤𝘪𝘴𝘪𝘰𝘯𝘴
🔹 𝘈𝘨𝘦𝘯𝘵𝘪𝘤 – 𝘓𝘓𝘔𝘴 𝘤𝘩𝘶𝘯𝘬 𝘭𝘪𝘬𝘦 𝘩𝘶𝘮𝘢𝘯𝘴
🔹 𝘚𝘮𝘢𝘭𝘭-𝘵𝘰-𝘉𝘪𝘨 – 𝘱𝘳𝘦𝘤𝘪𝘴𝘪𝘰𝘯 + 𝘤𝘰𝘯𝘵𝘦𝘹𝘵
🔹 𝘚𝘵𝘢𝘵𝘪𝘴𝘵𝘪𝘤𝘢𝘭 – 𝘥𝘢𝘵𝘢-𝘥𝘳𝘪𝘷𝘦𝘯 𝘣𝘰𝘶𝘯𝘥𝘢𝘳𝘪𝘦𝘴
🔹 𝘔𝘰𝘥𝘢𝘭𝘪𝘵𝘺-𝘚𝘱𝘦𝘤𝘪𝘧𝘪𝘤 – 𝘵𝘦𝘹𝘵, 𝘵𝘢𝘣𝘭𝘦𝘴, 𝘪𝘮𝘢𝘨𝘦𝘴, 𝘤𝘰𝘥𝘦
✨ 𝐏𝐫𝐨 𝐭𝐢𝐩: 𝐓𝐡𝐞𝐫𝐞’𝐬 𝐧𝐨 𝐨𝐧𝐞-𝐬𝐢𝐳𝐞-𝐟𝐢𝐭𝐬-𝐚𝐥𝐥. 𝐓𝐡𝐞 𝐛𝐞𝐬𝐭 𝐑𝐀𝐆 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐮𝐬𝐞 𝐡𝐲𝐛𝐫𝐢𝐝 𝐜𝐡𝐮𝐧𝐤𝐢𝐧𝐠 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 𝐭𝐚𝐢𝐥𝐨𝐫𝐞𝐝 𝐭𝐨 𝐭𝐡𝐞𝐢𝐫 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐧𝐝 𝐮𝐬𝐞 𝐜𝐚𝐬𝐞.
