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Machine Learning

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

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📈 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 40 191 名订阅者,在 技术与应用 类别中位列第 3 381,并在 叙利亚 地区排名第 228

📊 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 40 191 名订阅者。

根据 01 七月, 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 355,过去 24 小时变化为 21,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 2.04%。内容发布后 24 小时内通常能获得 2.12% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 818 次浏览,首日通常累积 851 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 02 七月, 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。

40 191
订阅者
+2124 小时
+857
+35530
帖子存档
📌 Structured Outputs and How to Use Them 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-09 | ⏱️ Read time: 5 min read
📌 Structured Outputs and How to Use Them 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-09 | ⏱️ Read time: 5 min read Building robustness and determinism in LLM applications

📌 Improving Code Quality During Data Transformation with Polars 🗂 Category: 🕒 Date: 2024-08-09 | ⏱️ Read time: 6 min read
📌 Improving Code Quality During Data Transformation with Polars 🗂 Category: 🕒 Date: 2024-08-09 | ⏱️ Read time: 6 min read Optimize your data workflows with Polars by improving code quality and refining transformations with these…

📌 Running a SOTA 7B Parameter Embedding Model on a Single GPU 🗂 Category: 🕒 Date: 2024-08-09 | ⏱️ Read time: 19 min read I
📌 Running a SOTA 7B Parameter Embedding Model on a Single GPU 🗂 Category: 🕒 Date: 2024-08-09 | ⏱️ Read time: 19 min read In this post I will explain how to run a state-of-the-art 7B parameter LLM based…

📌 Algorithm-Agnostic Model Building with MLflow 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-08-10 | ⏱️ Read time: 10 min rea
📌 Algorithm-Agnostic Model Building with MLflow 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-08-10 | ⏱️ Read time: 10 min read A beginner-friendly step-by-step guide to creating generic ML pipelines using mlflow.pyfunc

📌 Data Scaling 101: Standardization and Min-Max Scaling Explained 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-08-10 | ⏱️ Rea
📌 Data Scaling 101: Standardization and Min-Max Scaling Explained 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-08-10 | ⏱️ Read time: 5 min read When to use MinMaxScaler vs StandardScaler vs something else

📌 Which Regression technique should you use? 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-10 | ⏱️ Read time: 12 min
📌 Which Regression technique should you use? 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-10 | ⏱️ Read time: 12 min read Here’s a taxonomy of what is the best regression technique based on your specific dataset

📌 Denormalisation: Thoughtful Optimisation or Irrational Avant-Garde? 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-10 | ⏱️ Rea
📌 Denormalisation: Thoughtful Optimisation or Irrational Avant-Garde? 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-10 | ⏱️ Read time: 19 min read Perspective on Performance Optimisation and Data Quality

📌 Introduction to Support Vector Machines - Motivation and Basics 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-10 | ⏱️ Read ti
📌 Introduction to Support Vector Machines - Motivation and Basics 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-10 | ⏱️ Read time: 8 min read Learn basic concepts that make Support Vector Machine a powerful linear classifier

📌 Accelerating AI/ML Model Training with Custom Operators 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-08-11 | ⏱️ Read time:
📌 Accelerating AI/ML Model Training with Custom Operators 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-08-11 | ⏱️ Read time: 18 min read On the potential benefits of creating model-specific GPU kernels and their application to optimizing the…

📌 Top Career Websites for Data Engineers 🗂 Category: ANALYTICS 🕒 Date: 2024-08-11 | ⏱️ Read time: 9 min read How to find f
📌 Top Career Websites for Data Engineers 🗂 Category: ANALYTICS 🕒 Date: 2024-08-11 | ⏱️ Read time: 9 min read How to find fantastic remote jobs and get hired

What if every notification meant free money? Kittu X Earning reveals secret hacks, daily loot, and real ways to grow your ear
What if every notification meant free money? Kittu X Earning reveals secret hacks, daily loot, and real ways to grow your earning game. Ready to spot the trick that others always miss? Don’t let easy cash slip by — hit join and become part of the earning empire today! Timing matters. Start earning now ➔ Kittu X Earning 💸 #ad InsideAds

📌 How to practice data analyst interviews with AI 🗂 Category: 🕒 Date: 2024-08-12 | ⏱️ Read time: 8 min read Using LLMs to
📌 How to practice data analyst interviews with AI 🗂 Category: 🕒 Date: 2024-08-12 | ⏱️ Read time: 8 min read Using LLMs to generate synthetic data and code

📌 AI Agents – From Concepts to Practical Implementation in Python 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-12 |
📌 AI Agents – From Concepts to Practical Implementation in Python 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-12 | ⏱️ Read time: 12 min read This will change the way you think about AI and its capabilities

📌 Advanced Recursive and Follow-Up Retrieval Techniques For Better RAGs 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-0
📌 Advanced Recursive and Follow-Up Retrieval Techniques For Better RAGs 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-12 | ⏱️ Read time: 18 min read Breaking the problem solves half of it. Chaining them makes it even better.

📌 The Poisson Bootstrap 🗂 Category: STATISTICS 🕒 Date: 2024-08-12 | ⏱️ Read time: 10 min read Bootstrapping over large dat
📌 The Poisson Bootstrap 🗂 Category: STATISTICS 🕒 Date: 2024-08-12 | ⏱️ Read time: 10 min read Bootstrapping over large datasets

📌 New Approach for Training Physical (as Opposed to Computer-Based) Artificial Neural Networks 🗂 Category: ARTIFICIAL INTEL
📌 New Approach for Training Physical (as Opposed to Computer-Based) Artificial Neural Networks 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-12 | ⏱️ Read time: 7 min read Neural networks built from light waves could allow for much more versatile, scalable, and energy-efficient…

📌 LLM-Powered Parsing and Analysis of Semi-Structured & Structured Documents 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 202
📌 LLM-Powered Parsing and Analysis of Semi-Structured & Structured Documents 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-08-12 | ⏱️ Read time: 20 min read This article shows how to extract desired or key information from semi-structured or unstructured information…

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📌 My Honest Advice for Someone Who Wants to Become a Data Scientist 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-12
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