现已上线！2025 年 Telegram 研究 — 年度关键洞察 
Machine Learning
前往频道在 Telegram
Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho
显示更多📈 Telegram 频道 Machine Learning 的分析概览
频道 Machine Learning (@machinelearning9) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 40 106 名订阅者，在 技术与应用 类别中位列第 3 384，并在 叙利亚 地区排名第 231 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 40 106 名订阅者。
根据 24 六月, 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 401，过去 24 小时变化为 38，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 1.96%。内容发布后 24 小时内通常能获得 1.16% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 788 次浏览，首日通常累积 465 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 distance, insidead, gpu, learning, degree 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：
“Real Machine Learning — simple, practical, and built on experience.
Learn step by step with clear explanations and working code.
Admin: @HusseinSheikho || @Hussein_Sheikho”
凭借高频更新（最新数据采集于 25 六月, 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。
40 106
订阅者
+3824 小时
+637 天
+40130 天
帖子存档
40 109
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40 109
📌 Topic Modeling Techniques for 2026: Seeded Modeling, LLM Integration, and Data Summaries
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-01-14 | ⏱️ Read time: 15 min read
Seeded topic modeling, integration with LLMs, and training on summarized data are the fresh parts…
#DataScience #AI #Python
40 109
📌 Glitches in the Attention Matrix
🗂 Category: DEEP LEARNING
🕒 Date: 2026-01-14 | ⏱️ Read time: 13 min read
A history of Transformer artifacts and the latest research on how to fix them
#DataScience #AI #Python
40 109
Repost from Machine Learning with Python
Do you want to teach AI on real projects?
In this #repository, there are 29 projects with Generative #AI,#MachineLearning, and #Deep +Learning.
With full #code for each one. This is pure gold: https://github.com/KalyanM45/AI-Project-Gallery
👉 https://t.me/CodeProgrammer
40 109
📌 What Is a Knowledge Graph — and Why It Matters
🗂 Category: DATA SCIENCE
🕒 Date: 2026-01-14 | ⏱️ Read time: 18 min read
How structured knowledge became healthcare’s quiet advantage
#DataScience #AI #Python
40 109
📌 Why Human-Centered Data Analytics Matters More Than Ever
🗂 Category: DATA SCIENCE
🕒 Date: 2026-01-14 | ⏱️ Read time: 8 min read
From optimizing metrics to designing meaning: putting people back into data-driven decisions
#DataScience #AI #Python
40 109
📌 From ‘Dataslows’ to Dataflows: The Gen2 Performance Revolution in Microsoft Fabric
🗂 Category: DATA ENGINEERING
🕒 Date: 2026-01-13 | ⏱️ Read time: 8 min read
Dataflows were (rightly?) considered “the slowest and least performant option” for ingesting data into Power…
#DataScience #AI #Python
40 109
📌 An introduction to AWS Bedrock
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-01-13 | ⏱️ Read time: 13 min read
The how, why, what and where of Amazon’s LLM access layer
#DataScience #AI #Python
40 109
Repost from Machine Learning with Python
⚡️ All cheat sheets for programmers in one place.
There's a lot of useful stuff inside: short, clear tips on languages, technologies, and frameworks.
No registration required and it's free.
https://overapi.com/
#python #php #Database #DataAnalysis #MachineLearning #AI #DeepLearning #LLMS
https://t.me/CodeProgrammer ⚡️
40 109
📌 How to Maximize Claude Code Effectiveness
🗂 Category: AGENTIC AI
🕒 Date: 2026-01-13 | ⏱️ Read time: 9 min read
Learn how to get the most out of agentic coding
#DataScience #AI #Python
40 109
📌 Why Your ML Model Works in Training But Fails in Production
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-01-13 | ⏱️ Read time: 8 min read
Hard lessons from building production ML systems where data leaks, defaults lie, populations shift, and…
#DataScience #AI #Python
40 109
📌 Under the Uzès Sun: When Historical Data Reveals the Climate Change
🗂 Category: DATA SCIENCE
🕒 Date: 2026-01-13 | ⏱️ Read time: 11 min read
Longer summers, milder winters: analysis of temperature trends in Uzès, France, year after year.
#DataScience #AI #Python
40 109
📌 Optimizing Data Transfer in Batched AI/ML Inference Workloads
🗂 Category: DATA ENGINEERING
🕒 Date: 2026-01-12 | ⏱️ Read time: 13 min read
A deep dive on data transfer bottlenecks, their identification, and their resolution with the help…
#DataScience #AI #Python
40 109
📌 When Does Adding Fancy RAG Features Work?
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-01-12 | ⏱️ Read time: 23 min read
Looking at the performance of different pipelines
#DataScience #AI #Python
40 109
📌 Why 90% Accuracy in Text-to-SQL is 100% Useless
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-01-12 | ⏱️ Read time: 9 min read
The eternal promise of self-service analytics
#DataScience #AI #Python
40 109
Repost from Machine Learning with Python
+1
These Google Colab-notebooks help to implement all machine learning algorithms from scratch 🤯
Repo: https://udlbook.github.io/udlbook/
👉 @codeprogrammer
40 109
📌 How AI Can Become Your Personal Language Tutor
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-01-12 | ⏱️ Read time: 11 min read
How I used n8n to build AI study partners for learning Mandarin: vocabulary, listening, and…
#DataScience #AI #Python
40 109
Repost from Machine Learning with Python
🧠 𝐊-𝐍𝐞𝐚𝐫𝐞𝐬𝐭 𝐍𝐞𝐢𝐠𝐡𝐛𝐨𝐫𝐬 (𝐊𝐍𝐍)
🔹 𝐖𝐡𝐚𝐭 𝐈 𝐜𝐨𝐯𝐞𝐫𝐞𝐝 𝐭𝐨𝐝𝐚𝐲
𝐖𝐡𝐚𝐭 𝐊𝐍𝐍 𝐢𝐬 𝐚𝐧𝐝 𝐡𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬
𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐊𝐍𝐍 𝐟𝐨𝐫 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐯𝐬 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧
𝐑𝐨𝐥𝐞 𝐨𝐟 𝐊 (𝐡𝐲𝐩𝐞𝐫𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫)
𝐃𝐢𝐬𝐭𝐚𝐧𝐜𝐞 𝐦𝐞𝐭𝐫𝐢𝐜𝐬: 𝐄𝐮𝐜𝐥𝐢𝐝𝐞𝐚𝐧 𝐯𝐬 𝐌𝐚𝐧𝐡𝐚𝐭𝐭𝐚𝐧
𝐖𝐡𝐲 𝐊𝐍𝐍 𝐢𝐬 𝐜𝐚𝐥𝐥𝐞𝐝 𝐚 𝐥𝐚𝐳𝐲 / 𝐢𝐧𝐬𝐭𝐚𝐧𝐜𝐞-𝐛𝐚𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐞𝐫
🎯 𝐓𝐨𝐩 𝟏𝟎 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 (𝐌𝐮𝐬𝐭-𝐊𝐧𝐨𝐰)
1️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘒-𝘕𝘦𝘢𝘳𝘦𝘴𝘵 𝘕𝘦𝘪𝘨𝘩𝘣𝘰𝘳𝘴 (𝘒𝘕𝘕)?
2️⃣ 𝘞𝘩𝘺 𝘪𝘴 𝘒𝘕𝘕 𝘤𝘢𝘭𝘭𝘦𝘥 𝘢 𝘭𝘢𝘻𝘺 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘢𝘭𝘨𝘰𝘳𝘪𝘵𝘩𝘮?
3️⃣ 𝘋𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘤𝘦 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘒𝘕𝘕 𝘤𝘭𝘢𝘴𝘴𝘪𝘧𝘪𝘤𝘢𝘵𝘪𝘰𝘯 𝘢𝘯𝘥 𝘒𝘕𝘕 𝘳𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯?
4️⃣ 𝘏𝘰𝘸 𝘥𝘰 𝘺𝘰𝘶 𝘤𝘩𝘰𝘰𝘴𝘦 𝘵𝘩𝘦 𝘷𝘢𝘭𝘶𝘦 𝘰𝘧 𝘒?
5️⃣ 𝘞𝘩𝘢𝘵 𝘩𝘢𝘱𝘱𝘦𝘯𝘴 𝘸𝘩𝘦𝘯 𝘒 𝘪𝘴 𝘵𝘰𝘰 𝘴𝘮𝘢𝘭𝘭 𝘰𝘳 𝘵𝘰𝘰 𝘭𝘢𝘳𝘨𝘦?
6️⃣ 𝘞𝘩𝘢𝘵 𝘥𝘪𝘴𝘵𝘢𝘯𝘤𝘦 𝘮𝘦𝘵𝘳𝘪𝘤𝘴 𝘢𝘳𝘦 𝘤𝘰𝘮𝘮𝘰𝘯𝘭𝘺 𝘶𝘴𝘦𝘥 𝘪𝘯 𝘒𝘕𝘕?
7️⃣ 𝘞𝘩𝘺 𝘥𝘰𝘦𝘴 𝘒𝘕𝘕 𝘱𝘦𝘳𝘧𝘰𝘳𝘮 𝘱𝘰𝘰𝘳𝘭𝘺 𝘰𝘯 𝘩𝘪𝘨𝘩-𝘥𝘪𝘮𝘦𝘯𝘴𝘪𝘰𝘯𝘢𝘭 𝘥𝘢𝘵𝘢?
8️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘵𝘩𝘦 𝘵𝘪𝘮𝘦 𝘤𝘰𝘮𝘱𝘭𝘦𝘹𝘪𝘵𝘺 𝘰𝘧 𝘒𝘕𝘕?
9️⃣ 𝘏𝘰𝘸 𝘥𝘰 𝘒𝘋-𝘛𝘳𝘦𝘦 𝘢𝘯𝘥 𝘉𝘢𝘭𝘭-𝘛𝘳𝘦𝘦 𝘪𝘮𝘱𝘳𝘰𝘷𝘦 𝘒𝘕𝘕 𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦?
🔟 𝘞𝘩𝘦𝘯 𝘴𝘩𝘰𝘶𝘭𝘥 𝘺𝘰𝘶 𝘢𝘷𝘰𝘪𝘥 𝘶𝘴𝘪𝘯𝘨 #𝘒𝘕𝘕?
https://t.me/CodeProgrammer ⭐️
40 109
📌 How to Leverage Slash Commands to Code Effectively
🗂 Category: LLM APPLICATIONS
🕒 Date: 2026-01-11 | ⏱️ Read time: 8 min read
Learn how I utilize slash commands to be a more efficient engineer
#DataScience #AI #Python
