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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
📌 Visualising Strava Race Analysis 🗂 Category: 🕒 Date: 2024-08-06 | ⏱️ Read time: 17 min read Two New Graphs That Compare
📌 Visualising Strava Race Analysis 🗂 Category: 🕒 Date: 2024-08-06 | ⏱️ Read time: 17 min read Two New Graphs That Compare Runners on the Same Event

📌 Create Synthetic Dataset Using Llama 3.1 to Fine-Tune Your LLM 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-07 | ⏱️ Read tim
📌 Create Synthetic Dataset Using Llama 3.1 to Fine-Tune Your LLM 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-07 | ⏱️ Read time: 10 min read Using the giant Llama 3.1 405B and Nvidia Nemotron 4 reward model to create a…

📌 Stop Wasting LLM Tokens 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-07 | ⏱️ Read time: 5 min read Batching your inputs toge
📌 Stop Wasting LLM Tokens 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-07 | ⏱️ Read time: 5 min read Batching your inputs together can lead to substantial savings without compromising on performance

📌 Strategizing Your Preparation for Machine Learning Interviews 🗂 Category: CAREER ADVICE 🕒 Date: 2024-08-07 | ⏱️ Read tim
📌 Strategizing Your Preparation for Machine Learning Interviews 🗂 Category: CAREER ADVICE 🕒 Date: 2024-08-07 | ⏱️ Read time: 10 min read Decoding Job Roles and identify focus areas

📌 High-Performance Data Processing: pandas 2 vs. Polars, a vCPU Perspective 🗂 Category: 🕒 Date: 2024-08-07 | ⏱️ Read time:
📌 High-Performance Data Processing: pandas 2 vs. Polars, a vCPU Perspective 🗂 Category: 🕒 Date: 2024-08-07 | ⏱️ Read time: 8 min read Polars promises its multithreading capabilities outperform pandas. But is it also the case with a…

📌 Short and Sweet: Enhancing LLM Performance with Constrained Chain-of-Thought 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date:
📌 Short and Sweet: Enhancing LLM Performance with Constrained Chain-of-Thought 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-07 | ⏱️ Read time: 10 min read Sometimes few words are enough: reducing output length for increasing accuracy

📌 AI Shapeshifters: The Changing Role of the AI Engineer and Applied Data Scientist 🗂 Category: DATA SCIENCE 🕒 Date: 2024-
📌 AI Shapeshifters: The Changing Role of the AI Engineer and Applied Data Scientist 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-07 | ⏱️ Read time: 5 min read The role of AI Engineer and Applied Data Scientist has undergone a remarkable transformation. Where…

📌 Reinforcement Learning, Part 6: n-step Bootstrapping 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-07 | ⏱️ Read ti
📌 Reinforcement Learning, Part 6: n-step Bootstrapping 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-07 | ⏱️ Read time: 7 min read Pushing the boundaries: generalizing temporal difference algorithms

📌 Spatial Interpolation in Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-08 | ⏱️ Read time: 4 min read Using the Inverse
📌 Spatial Interpolation in Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-08 | ⏱️ Read time: 4 min read Using the Inverse Distance Weighting method to infer missing spatial data

📌 How to Use Machine Learning to Inform Design Decisions and Make Predictions 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-08
📌 How to Use Machine Learning to Inform Design Decisions and Make Predictions 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-08 | ⏱️ Read time: 15 min read An Introductory Guide and Use Case for Applied Data Science

📌 5 Proven Query Translation Techniques To Boost Your RAG Performance 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-
📌 5 Proven Query Translation Techniques To Boost Your RAG Performance 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-08 | ⏱️ Read time: 11 min read How to get near-perfect LLM performance even with ambiguous user inputs

📌 The Big Questions Shaping AI Today 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-08 | ⏱️ Read time: 4 min read Our
📌 The Big Questions Shaping AI Today 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-08 | ⏱️ Read time: 4 min read Our weekly selection of must-read Editors’ Picks and original features

📌 3 Key Tweaks That Will Make Your Matplotlib Charts Publication Ready 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-08 | ⏱️ Re
📌 3 Key Tweaks That Will Make Your Matplotlib Charts Publication Ready 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-08 | ⏱️ Read time: 4 min read Matplotlib charts are an eyesore by default – here’s what to do about it.

📌 Ask Not What AI Can Do for You – Ask What You Can Achieve with AI 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-08
📌 Ask Not What AI Can Do for You – Ask What You Can Achieve with AI 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-08 | ⏱️ Read time: 11 min read Unlock AI for Everyone: Discover How You Can Use LLMs in Everyday Tasks

📌 Create Stronger Decision Trees with bootstrapping and genetic algorithms 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 202
📌 Create Stronger Decision Trees with bootstrapping and genetic algorithms 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-09 | ⏱️ Read time: 31 min read A technique to better allow decision trees to be used as interpretable models

📌 We Need to Raise the Bar for AI Product Managers 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-09 | ⏱️ Read time:
📌 We Need to Raise the Bar for AI Product Managers 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-09 | ⏱️ Read time: 10 min read How to Stop Blaming the ‘Model’ and Start Building Successful AI Products

📌 LLMOps – Serve a Llama-3 model with BentoML 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-09 | ⏱️ Read time: 5 min
📌 LLMOps – Serve a Llama-3 model with BentoML 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-09 | ⏱️ Read time: 5 min read Quickly set up LLM APIs with BentoML and Runpod

📌 AI for the Absolute Novice – Intuitively and Exhaustively Explained 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-
📌 AI for the Absolute Novice – Intuitively and Exhaustively Explained 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-09 | ⏱️ Read time: 40 min read From “I’ve never coded” to making an AI model from scratch.

📌 KernelSHAP can be misleading with correlated predictors 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-09 | ⏱️ Read
📌 KernelSHAP can be misleading with correlated predictors 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-09 | ⏱️ Read time: 7 min read A concrete case study

📌 Pre-Commit & Git Hooks: Automate High Code Quality 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-09 | ⏱️ Read time
📌 Pre-Commit & Git Hooks: Automate High Code Quality 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-09 | ⏱️ Read time: 6 min read How to improve your code quality with pre-commit and git hooks

Machine Learning - Telegram 频道 @machinelearning9 的统计与分析