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
شبکه:📈 تحلیل کانال تلگرام Machine Learning
کانال Machine Learning (@machinelearning9) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 40 334 مشترک است و جایگاه 3 331 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 40 334 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 10 ژوئیه, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 383 و در ۲۴ ساعت گذشته برابر 25 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 2.35% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.95% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 948 بازدید دریافت میکند. در اولین روز معمولاً 786 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 11 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.
40 334
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آرشیو پست ها
40 334
📌 Will You Spot the Leaks? A Data Science Challenge
🗂 Category: DATA SCIENCE
🕒 Date: 2025-05-12 | ⏱️ Read time: 8 min read
When models fly too high: A perilous journey through data leakage
40 334
📌 Running Python Programs in Your Browser
🗂 Category: PROGRAMMING
🕒 Date: 2025-05-12 | ⏱️ Read time: 17 min read
Using Pyodide and Webassembly
40 334
📌 Pause Your ML Pipelines for Human Review Using AWS Step Functions + Slack
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-05-12 | ⏱️ Read time: 7 min read
Build trust into your machine learning pipelines by inserting fast, secure human checks.
40 334
📌 The Westworld Blunder
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2025-05-12 | ⏱️ Read time: 16 min read
Giving artificial minds the appearance of suffering without the awareness that it’s just a performance…
40 334
📌 How I Finally Understood MCP — and Got It Working in Real Life
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-05-12 | ⏱️ Read time: 28 min read
The guide I needed when I had no idea why anyone would build an MCP…
40 334
📌 Empowering LLMs to Think Deeper by Erasing Thoughts
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-05-12 | ⏱️ Read time: 11 min read
Introduction Recent large language models (LLMs) — such as OpenAI’s o1/o3, DeepSeek’s R1 and Anthropic’s…
40 334
📌 TDS Authors Can Now Receive Payments Via Stripe
🗂 Category: WRITING
🕒 Date: 2025-05-13 | ⏱️ Read time: 2 min read
The Author Payment Program just became a lot more streamlined
40 334
📌 Rethinking the Environmental Costs of Training AI — Why We Should Look Beyond Hardware
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2025-05-13 | ⏱️ Read time: 19 min read
A statistical analysis of what drives energy, water, and carbon consumption in AI training —…
40 334
📌 Non-Parametric Density Estimation: Theory and Applications
🗂 Category: STATISTICS
🕒 Date: 2025-05-13 | ⏱️ Read time: 26 min read
A theoretical and practical introduction to non-parametric density estimation.
40 334
📌 Get Started with Rust: Installation and Your First CLI Tool – A Beginner’s Guide
🗂 Category: PROGRAMMING
🕒 Date: 2025-05-13 | ⏱️ Read time: 8 min read
From setup to your first command line application — step by step
40 334
📌 Survival Analysis When No One Dies: A Value-Based Approach
🗂 Category: STATISTICS
🕒 Date: 2025-05-13 | ⏱️ Read time: 11 min read
A generalized version of Kaplan-Meier allows to model a continuous value (like money) instead of…
40 334
📌 Parquet File Format – Everything You Need to Know!
🗂 Category: DATA ENGINEERING
🕒 Date: 2025-05-14 | ⏱️ Read time: 9 min read
New data flavors require new ways for storing it! Learn everything you need to know…
40 334
📌 Efficient Graph Storage for Entity Resolution Using Clique-Based Compression
🗂 Category: DATA SCIENCE
🕒 Date: 2025-05-14 | ⏱️ Read time: 7 min read
Entity resolution systems face challenges with dense, interconnected graphs, and clique-based graph compression offers an…
40 334
📌 Strength in Numbers: Ensembling Models with Bagging and Boosting
🗂 Category: DATA SCIENCE
🕒 Date: 2025-05-15 | ⏱️ Read time: 16 min read
Mastering the fundamentals of bagging and boosting with simple examples
40 334
📌 The Geospatial Capabilities of Microsoft Fabric and ESRI GeoAnalytics, Demonstrated
🗂 Category: DATA ENGINEERING
🕒 Date: 2025-05-15 | ⏱️ Read time: 8 min read
A step closer to spatial AI with geospatial processing with Fabric
40 334
📌 Boost 2-Bit LLM Accuracy with EoRA
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-05-15 | ⏱️ Read time: 9 min read
A training-free solution for extreme LLM compression.
40 334
📌 Explore the New World of Agent Protocols
🗂 Category: THE VARIABLE
🕒 Date: 2025-05-15 | ⏱️ Read time: 3 min read
This week, we focus on helping you gain a deeper understanding of MCP and other…
40 334
📌 How to Learn the Math Needed for Machine Learning
🗂 Category: MATH
🕒 Date: 2025-05-15 | ⏱️ Read time: 7 min read
A breakdown of the three fundamental math fields required for machine learning: statistics, linear algebra,…
40 334
📌 Understanding Random Forest using Python (scikit-learn)
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-05-15 | ⏱️ Read time: 9 min read
A Random Forest is a powerful machine learning algorithm that can be used for classification…
40 334
📌 Google’s AlphaEvolve Is Evolving New Algorithms — And It Could Be a Game Changer
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2025-05-15 | ⏱️ Read time: 6 min read
A blend of LLMs’ creative generation capabilities with genetic algorithms
