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 40 072 مشترک است و جایگاه 3 398 را در دسته فناوری و برنامه‌ها و رتبه 232 را در منطقه سوريا دارد.

📊 شاخص‌های مخاطب و پویایی

از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 40 072 مشترک جذب کرده است.

بر اساس آخرین داده‌ها در تاریخ 23 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 379 و در ۲۴ ساعت گذشته برابر 30 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.

  • وضعیت تأیید: تأیید نشده
  • نرخ تعامل (ER): میانگین تعامل مخاطب 1.92% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.16% واکنش نسبت به کل مشترکان کسب می‌کند.
  • دسترسی پست‌ها: هر پست به طور میانگین 770 بازدید دریافت می‌کند. در اولین روز معمولاً 466 بازدید جمع‌آوری می‌شود.
  • واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 3 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 24 ژوئن, 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.

40 072
مشترکین
+3024 ساعت
+337 روز
+37930 روز
آرشیو پست ها
📌 The Machine Learning Lessons I’ve Learned This Month 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-03-02 | ⏱️ Read time: 6 m
📌 The Machine Learning Lessons I’ve Learned This Month 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-03-02 | ⏱️ Read time: 6 min read February 2026: exchange with others, documentation, and MLOps #DataScience #AI #Python

Excellent free courses on neural networks from Nvidia— the company decided to share knowledge that usually costs 90 dollars.
Excellent free courses on neural networks from Nvidia— the company decided to share knowledge that usually costs 90 dollars. Here's everything important: video processing, app development, robotics, and much more. An electronic certificate is issued upon completion of the training. We gain useful knowledge — https://developer.nvidia.com/join-nvidia-developer-program https://t.me/CodeProgrammer 🌟

📌 YOLOv3 Paper Walkthrough: Even Better, But Not That Much 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-02 | ⏱️ Rea
📌 YOLOv3 Paper Walkthrough: Even Better, But Not That Much 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-02 | ⏱️ Read time: 24 min read A PyTorch implementation on the YOLOv3 architecture from scratch #DataScience #AI #Python

📌 Exciting Changes Are Coming to the TDS Author Payment Program 🗂 Category: WRITING 🕒 Date: 2026-03-02 | ⏱️ Read time: 2 m
📌 Exciting Changes Are Coming to the TDS Author Payment Program 🗂 Category: WRITING 🕒 Date: 2026-03-02 | ⏱️ Read time: 2 min read Authors can now benefit from updated earning tiers and a higher article cap #DataScience #AI #Python

📌 Context Engineering as Your Competitive Edge 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-03-01 | ⏱️ Read time: 13 min
📌 Context Engineering as Your Competitive Edge 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-03-01 | ⏱️ Read time: 13 min read If you have both unique domain expertise and know how to make it usable to… #DataScience #AI #Python

📌 Zero-Waste Agentic RAG: Designing Caching Architectures to Minimize Latency and LLM Costs at Scale 🗂 Category: LARGE LANG
📌 Zero-Waste Agentic RAG: Designing Caching Architectures to Minimize Latency and LLM Costs at Scale 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-03-01 | ⏱️ Read time: 19 min read Reducing LLM costs by 30% with validation-aware, multi-tier caching #DataScience #AI #Python

📌 Scaling ML Inference on Databricks: Liquid or Partitioned? Salted or Not? 🗂 Category: DATA ENGINEERING 🕒 Date: 2026-02-2
📌 Scaling ML Inference on Databricks: Liquid or Partitioned? Salted or Not? 🗂 Category: DATA ENGINEERING 🕒 Date: 2026-02-28 | ⏱️ Read time: 11 min read A case study on techniques to maximize your clusters #DataScience #AI #Python

Double your first deposit with up to 100% bonus at top 5 casinos. Verified offers only. Start winning smarter: Casino Bonus H
Double your first deposit with up to 100% bonus at top 5 casinos. Verified offers only. Start winning smarter: Casino Bonus Hub #ad InsideAds

📌 Claude Skills and Subagents: Escaping the Prompt Engineering Hamster Wheel 🗂 Category: AGENTIC AI 🕒 Date: 2026-02-28 | ⏱
📌 Claude Skills and Subagents: Escaping the Prompt Engineering Hamster Wheel 🗂 Category: AGENTIC AI 🕒 Date: 2026-02-28 | ⏱️ Read time: 17 min read How reusable, lazy-loaded instructions solve the context bloat problem in AI-assisted development. #DataScience #AI #Python

📌 The Gap Between Junior and Senior Data Scientists Isn’t Code 🗂 Category: DATA SCIENCE 🕒 Date: 2026-02-27 | ⏱️ Read time:
📌 The Gap Between Junior and Senior Data Scientists Isn’t Code 🗂 Category: DATA SCIENCE 🕒 Date: 2026-02-27 | ⏱️ Read time: 6 min read Why my obsession with complex algorithms was actually holding my career back. #DataScience #AI #Python

📌 Generative AI, Discriminative Human 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-02-27 | ⏱️ Read time: 14 min read H
📌 Generative AI, Discriminative Human 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-02-27 | ⏱️ Read time: 14 min read How to think critically about AI in an ocean of hype #DataScience #AI #Python

📌 Stop Asking if a Model Is Interpretable 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-02-27 | ⏱️ Read time: 6 min rea
📌 Stop Asking if a Model Is Interpretable 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-02-27 | ⏱️ Read time: 6 min read Start asking what question the explanation should answer. #DataScience #AI #Python

📌 Coding the Pong Game from Scratch in Python 🗂 Category: PROGRAMMING 🕒 Date: 2026-02-27 | ⏱️ Read time: 18 min read Imple
📌 Coding the Pong Game from Scratch in Python 🗂 Category: PROGRAMMING 🕒 Date: 2026-02-27 | ⏱️ Read time: 18 min read Implementing the classic Pong game in Python using OOP and Turtle #DataScience #AI #Python

📌 Take a Deep Dive into Filtering in DAX 🗂 Category: DATA ANALYSIS 🕒 Date: 2026-02-26 | ⏱️ Read time: 13 min read Have you
📌 Take a Deep Dive into Filtering in DAX 🗂 Category: DATA ANALYSIS 🕒 Date: 2026-02-26 | ⏱️ Read time: 13 min read Have you ever wondered what happens when you apply a filter in a DAX expression?… #DataScience #AI #Python

📌 Designing Data and AI Systems That Hold Up in Production 🗂 Category: AUTHOR SPOTLIGHTS 🕒 Date: 2026-02-26 | ⏱️ Read time
📌 Designing Data and AI Systems That Hold Up in Production 🗂 Category: AUTHOR SPOTLIGHTS 🕒 Date: 2026-02-26 | ⏱️ Read time: 6 min read A system-level perspective on architecture, agents, and responsible scale #DataScience #AI #Python

Free access to over 40 courses https://lve.to/jwxfnss0yi

📌 Detecting and Editing Visual Objects with Gemini 🗂 Category: LLM APPLICATIONS 🕒 Date: 2026-02-26 | ⏱️ Read time: 34 min
📌 Detecting and Editing Visual Objects with Gemini 🗂 Category: LLM APPLICATIONS 🕒 Date: 2026-02-26 | ⏱️ Read time: 34 min read A practical guide to identifying, restoring, and transforming elements within your images #DataScience #AI #Python

📌 A Generalizable MARL-LP Approach for Scheduling in Logistics 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-02-26 | ⏱️ Read t
📌 A Generalizable MARL-LP Approach for Scheduling in Logistics 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-02-26 | ⏱️ Read time: 17 min read Part 1. Hybrid Solution for Dynamic Vehicle Routing — Context and Architecture #DataScience #AI #Python

📌 Breaking the Host Memory Bottleneck: How Peer Direct Transformed Gaudi’s Cloud Performance 🗂 Category: ARTIFICIAL INTELLI
📌 Breaking the Host Memory Bottleneck: How Peer Direct Transformed Gaudi’s Cloud Performance 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-02-25 | ⏱️ Read time: 9 min read Engineering RDMA-like performance over cloud host NICs using libfabric, DMA-BUF, and HCCL to restore distributed… #DataScience #AI #Python

📌 Scaling Feature Engineering Pipelines with Feast and Ray 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-02-25 | ⏱️ Read time:
📌 Scaling Feature Engineering Pipelines with Feast and Ray 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-02-25 | ⏱️ Read time: 11 min read Utilizing feature stores like Feast and distributed compute frameworks like Ray in production machine learning systems #DataScience #AI #Python