fa
Feedback
Machine Learning

Machine Learning

رفتن به کانال در Telegram

Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

نمایش بیشتر

📈 تحلیل کانال تلگرام Machine Learning

کانال Machine Learning (@machinelearning9) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 40 373 مشترک است و جایگاه 3 327 را در دسته فناوری و برنامه‌ها و رتبه 225 را در منطقه سوريا دارد.

📊 شاخص‌های مخاطب و پویایی

از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 40 373 مشترک جذب کرده است.

بر اساس آخرین داده‌ها در تاریخ 12 ژوئیه, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 399 و در ۲۴ ساعت گذشته برابر 24 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.

  • وضعیت تأیید: تأیید نشده
  • نرخ تعامل (ER): میانگین تعامل مخاطب 2.42% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.74% واکنش نسبت به کل مشترکان کسب می‌کند.
  • دسترسی پست‌ها: هر پست به طور میانگین 979 بازدید دریافت می‌کند. در اولین روز معمولاً 703 بازدید جمع‌آوری می‌شود.
  • واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 4 است.
  • علایق موضوعی: محتوا بر موضوعات کلیدی مانند distance, insidead, gpu, learning, degree تمرکز دارد.

📝 توضیح و سیاست محتوایی

نویسنده این فضا را محل بیان دیدگاه‌های شخصی توصیف می‌کند:
Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

به لطف به‌روزرسانی‌های پرتکرار (آخرین داده در تاریخ 13 ژوئیه, 2026)، کانال همواره به‌روز و دارای دسترسی بالاست. تحلیل‌ها نشان می‌دهد مخاطبان به‌طور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامه‌ها تبدیل کرده‌اند.

40 373
مشترکین
+2424 ساعت
+1257 روز
+39930 روز
آرشیو پست ها
📌 AI FOMO, Shadow AI, and Other Business Problems 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-09-03 | ⏱️ Read time: 6
📌 AI FOMO, Shadow AI, and Other Business Problems 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-09-03 | ⏱️ Read time: 6 min read What’s the state of AI in business these days, and how much does it cost…

📌 Useful Python Libraries You Might Not Have Heard Of: Freezegun 🗂 Category: PROGRAMMING 🕒 Date: 2025-09-03 | ⏱️ Read time
📌 Useful Python Libraries You Might Not Have Heard Of:  Freezegun 🗂 Category: PROGRAMMING 🕒 Date: 2025-09-03 | ⏱️ Read time: 12 min read Bring time to a standstill in your Python tests

📌 The Programming Skills You Need for Today’s Data Roles 🗂 Category: THE VARIABLE 🕒 Date: 2025-09-04 | ⏱️ Read time: 3 min
📌 The Programming Skills You Need for Today’s Data Roles 🗂 Category: THE VARIABLE 🕒 Date: 2025-09-04 | ⏱️ Read time: 3 min read How to stand out in a crowded field

📌 Boosting Your Anomaly Detection With LLMs 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-04 | ⏱️ Read time: 17 min re
📌 Boosting Your Anomaly Detection With LLMs 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-04 | ⏱️ Read time: 17 min read The 7 emerging application patterns you should know

📌 Using LangGraph and MCP Servers to Create My Own Voice Assistant 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-09-04 | ⏱️ Re
📌 Using LangGraph and MCP Servers to Create My Own Voice Assistant 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-09-04 | ⏱️ Read time: 30 min read Built over 14 days, all locally run, no API keys, cloud services, or subscription fees.

📌 MobileNetV1 Paper Walkthrough: The Tiny Giant 🗂 Category: DEEP LEARNING 🕒 Date: 2025-09-04 | ⏱️ Read time: 26 min read U
📌 MobileNetV1 Paper Walkthrough: The Tiny Giant 🗂 Category: DEEP LEARNING 🕒 Date: 2025-09-04 | ⏱️ Read time: 26 min read Understanding and implementing MobileNetV1 from scratch with PyTorch

📌 A Visual Guide to Tuning Random Forest Hyperparameters 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-04 | ⏱️ Read time: 8 min
📌 A Visual Guide to Tuning Random Forest Hyperparameters 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-04 | ⏱️ Read time: 8 min read How hyperparameter tuning visually changes random forests

📌 Should We Use LLMs As If They Were Swiss Knives? 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-09-04 | ⏱️ Read time:
📌 Should We Use LLMs As If They Were Swiss Knives? 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-09-04 | ⏱️ Read time: 9 min read A logic game performance comparison between popular LLMs and a custom-made algorithm

📌 Tool Masking: The Layer MCP Forgot 🗂 Category: AGENTIC AI 🕒 Date: 2025-09-05 | ⏱️ Read time: 16 min read Tool masking fo
📌 Tool Masking: The Layer MCP Forgot 🗂 Category: AGENTIC AI 🕒 Date: 2025-09-05 | ⏱️ Read time: 16 min read Tool masking for AI improves AI agents: shape MCP tool surfaces to cut tokens and…

📌 Zero-Inflated Data: A Comparison of Regression Models 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-05 | ⏱️ Read time: 13 min
📌 Zero-Inflated Data: A Comparison of Regression Models 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-05 | ⏱️ Read time: 13 min read How to detect it and which model to choose.

📌 AI Operations Under the Hood: Challenges and Best Practices 🗂 Category: LLM APPLICATIONS 🕒 Date: 2025-09-05 | ⏱️ Read ti
📌 AI Operations Under the Hood: Challenges and Best Practices 🗂 Category: LLM APPLICATIONS 🕒 Date: 2025-09-05 | ⏱️ Read time: 18 min read Building robust, reproducible, and reliable GenAI applications requires a framework of continuous improvement, rigorous evaluation,…

📌 Showcasing Your Work on HuggingFace Spaces 🗂 Category: PRODUCTIVITY 🕒 Date: 2025-09-05 | ⏱️ Read time: 9 min read Buildi
📌 Showcasing Your Work on HuggingFace Spaces 🗂 Category: PRODUCTIVITY 🕒 Date: 2025-09-05 | ⏱️ Read time: 9 min read Building an app is exciting – but sharing it is where the real value kicks…

📌 How to Context Engineer to Optimize Question Answering Pipelines 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-05 |
📌 How to Context Engineer to Optimize Question Answering Pipelines 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-05 | ⏱️ Read time: 9 min read Learn how to apply context engineering to enhance your question answering systems.

📌 AI FOMO, Shadow AI, and Other Business Problems 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-09-03 | ⏱️ Read time: 6
📌 AI FOMO, Shadow AI, and Other Business Problems 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-09-03 | ⏱️ Read time: 6 min read What’s the state of AI in business these days, and how much does it cost…

📌 Useful Python Libraries You Might Not Have Heard Of: Freezegun 🗂 Category: PROGRAMMING 🕒 Date: 2025-09-03 | ⏱️ Read time
📌 Useful Python Libraries You Might Not Have Heard Of:  Freezegun 🗂 Category: PROGRAMMING 🕒 Date: 2025-09-03 | ⏱️ Read time: 12 min read Bring time to a standstill in your Python tests

📌 The Programming Skills You Need for Today’s Data Roles 🗂 Category: THE VARIABLE 🕒 Date: 2025-09-04 | ⏱️ Read time: 3 min
📌 The Programming Skills You Need for Today’s Data Roles 🗂 Category: THE VARIABLE 🕒 Date: 2025-09-04 | ⏱️ Read time: 3 min read How to stand out in a crowded field

📌 Boosting Your Anomaly Detection With LLMs 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-04 | ⏱️ Read time: 17 min re
📌 Boosting Your Anomaly Detection With LLMs 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-04 | ⏱️ Read time: 17 min read The 7 emerging application patterns you should know

📌 Using LangGraph and MCP Servers to Create My Own Voice Assistant 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-09-04 | ⏱️ Re
📌 Using LangGraph and MCP Servers to Create My Own Voice Assistant 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-09-04 | ⏱️ Read time: 30 min read Built over 14 days, all locally run, no API keys, cloud services, or subscription fees.

📌 MobileNetV1 Paper Walkthrough: The Tiny Giant 🗂 Category: DEEP LEARNING 🕒 Date: 2025-09-04 | ⏱️ Read time: 26 min read U
📌 MobileNetV1 Paper Walkthrough: The Tiny Giant 🗂 Category: DEEP LEARNING 🕒 Date: 2025-09-04 | ⏱️ Read time: 26 min read Understanding and implementing MobileNetV1 from scratch with PyTorch

📌 A Visual Guide to Tuning Random Forest Hyperparameters 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-04 | ⏱️ Read time: 8 min
📌 A Visual Guide to Tuning Random Forest Hyperparameters 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-04 | ⏱️ Read time: 8 min read How hyperparameter tuning visually changes random forests