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
Machine Learning with Pythonشبکه:Machine Learning with Pythonسوريا227فناوری و برنامه‌ها3 364

📈 تحلیل کانال تلگرام Machine Learning

کانال Machine Learning (@machinelearning9) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 40 150 مشترک است و جایگاه 3 364 را در دسته فناوری و برنامه‌ها و رتبه 227 را در منطقه سوريا دارد.

📊 شاخص‌های مخاطب و پویایی

از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 40 150 مشترک جذب کرده است.

بر اساس آخرین داده‌ها در تاریخ 27 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 412 و در ۲۴ ساعت گذشته برابر 5 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.

  • وضعیت تأیید: تأیید نشده
  • نرخ تعامل (ER): میانگین تعامل مخاطب 1.96% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.89% واکنش نسبت به کل مشترکان کسب می‌کند.
  • دسترسی پست‌ها: هر پست به طور میانگین 785 بازدید دریافت می‌کند. در اولین روز معمولاً 760 بازدید جمع‌آوری می‌شود.
  • واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 2 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 28 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.

40 150
مشترکین
+524 ساعت
+1067 روز
+41230 روز
آرشیو پست ها
Machine Learning
Machine Learningverified-telegram-icon
40 146
🤖🧠 NVIDIA, MIT, HKU and Tsinghua University Introduce QeRL: A Powerful Quantum Leap in Reinforcement Learning for LLMs 🗓️
🤖🧠 NVIDIA, MIT, HKU and Tsinghua University Introduce QeRL: A Powerful Quantum Leap in Reinforcement Learning for LLMs 🗓️ 17 Oct 2025 📚 AI News & Trends The rise of large language models (LLMs) has redefined artificial intelligence powering everything from conversational AI to autonomous reasoning systems. However, training these models especially through reinforcement learning (RL) is computationally expensive requiring massive GPU resources and long training cycles. To address this, a team of researchers from NVIDIA, Massachusetts Institute of Technology (MIT), The ... #QuantumLearning #ReinforcementLearning #LLMs #NVIDIA #MIT #TsinghuaUniversity

Machine Learning
Machine Learningverified-telegram-icon
40 146
📌 How I Built an LLM-Based Game from Scratch 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-11 | ⏱️ Read time: 17 min
📌 How I Built an LLM-Based Game from Scratch 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-11 | ⏱️ Read time: 17 min read Part I: Game concepts and Causal Graphs for LLMs

Machine Learning
Machine Learningverified-telegram-icon
40 146
📌 Optimize Production with R - Part I 🗂 Category: 🕒 Date: 2024-06-11 | ⏱️ Read time: 8 min read An introduction to linear
📌 Optimize Production with R - Part I 🗂 Category: 🕒 Date: 2024-06-11 | ⏱️ Read time: 8 min read An introduction to linear programming with R

Machine Learning
Machine Learningverified-telegram-icon
40 146
📌 Beyond FOMO – Keeping up to date in AI 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-11 | ⏱️ Read time: 9 min read Don’t get
📌 Beyond FOMO – Keeping up to date in AI 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-11 | ⏱️ Read time: 9 min read Don’t get stressed but enjoy the journey.

Machine Learning
Machine Learningverified-telegram-icon
40 146
📌 Multi-Head Attention – Formally Explained and Defined 🗂 Category: DEEP LEARNING 🕒 Date: 2024-06-11 | ⏱️ Read time: 10 mi
📌 Multi-Head Attention – Formally Explained and Defined 🗂 Category: DEEP LEARNING 🕒 Date: 2024-06-11 | ⏱️ Read time: 10 min read A comprehensive and detailed formalization of multi-head attention.

Machine Learning
Machine Learningverified-telegram-icon
40 146
📌 How to Maximize Your Impact as a Data Scientist 🗂 Category: ANALYTICS 🕒 Date: 2024-06-11 | ⏱️ Read time: 13 min read Act
📌 How to Maximize Your Impact as a Data Scientist 🗂 Category: ANALYTICS 🕒 Date: 2024-06-11 | ⏱️ Read time: 13 min read Actionable advice to accelerate your career

Machine Learning
Machine Learningverified-telegram-icon
40 146
📌 Key Roles in a Fraud Prediction project with Machine Learning 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-06-11 | ⏱️ Read
📌 Key Roles in a Fraud Prediction project with Machine Learning 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-06-11 | ⏱️ Read time: 6 min read What type of roles are involved in developing a ML model for fraud prediction?

Machine Learning
Machine Learningverified-telegram-icon
40 146
📌 An Open Data-Driven Approach to Optimising Healthcare Facility Locations Using Python 🗂 Category: 🕒 Date: 2024-06-11 | ⏱
📌 An Open Data-Driven Approach to Optimising Healthcare Facility Locations Using Python 🗂 Category: 🕒 Date: 2024-06-11 | ⏱️ Read time: 15 min read A tutorial in Python with an open data stack

Machine Learning
Machine Learningverified-telegram-icon
40 146
📌 MLOps – Data Validation with PyTest 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-11 | ⏱️ Read time: 12 min read Run determin
📌 MLOps – Data Validation with PyTest 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-11 | ⏱️ Read time: 12 min read Run deterministic and non-deterministic tests to validate your dataset

Machine Learning
Machine Learningverified-telegram-icon
40 146
📌 ASA’s Caution: Rethinking How We Use p-Values in Research 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-11 | ⏱️ Read time: 9
📌 ASA’s Caution: Rethinking How We Use p-Values in Research 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-11 | ⏱️ Read time: 9 min read Understanding the ASA’s statement to enhance your data science practices

Machine Learning
Machine Learningverified-telegram-icon
40 146
📌 Deep Learning Illustrated, Part 4: Recurrent Neural Networks 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-11 | ⏱️
📌 Deep Learning Illustrated, Part 4: Recurrent Neural Networks 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-11 | ⏱️ Read time: 17 min read An illustrated and intuitive guide on the inner workings of an RNN and the Softmax…

Machine Learning
Machine Learningverified-telegram-icon
40 146
📌 Spatial Index: Grid Systems 🗂 Category: DATABASE DESIGN 🕒 Date: 2024-06-12 | ⏱️ Read time: 12 min read Grid Systems in S
📌 Spatial Index: Grid Systems 🗂 Category: DATABASE DESIGN 🕒 Date: 2024-06-12 | ⏱️ Read time: 12 min read Grid Systems in Spatial Indexing using GeoHash and Google S2

Machine Learning
Machine Learningverified-telegram-icon
40 146
📌 The Math Behind KAN – Kolmogorov-Arnold Networks 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-12 | ⏱️ Read time: 15 min read
📌 The Math Behind KAN – Kolmogorov-Arnold Networks 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-12 | ⏱️ Read time: 15 min read A new alternative to the classic Multi-Layer Perceptron is out. Why is it more accurate…

Machine Learning
Machine Learningverified-telegram-icon
40 146
📌 How to Pivot Tables in SQL 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-12 | ⏱️ Read time: 12 min read A comprehensive guide
📌 How to Pivot Tables in SQL 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-12 | ⏱️ Read time: 12 min read A comprehensive guide to creating pivot tables in SQL for enhanced data analysis

Machine Learning
Machine Learningverified-telegram-icon
40 146
📌 Model Interpretability Using Credit Card Fraud Data 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-12 | ⏱️ Read time: 20 min r
📌 Model Interpretability Using Credit Card Fraud Data 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-12 | ⏱️ Read time: 20 min read Why model interpretability is important

Machine Learning
Machine Learningverified-telegram-icon
40 146
📌 Simplifying the Python Code for Data Engineering Projects 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-06-12 | ⏱️ Read time
📌 Simplifying the Python Code for Data Engineering Projects 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-06-12 | ⏱️ Read time: 12 min read Python tricks and techniques for data ingestion, validation, processing, and testing: a practical walkthrough

Machine Learning
Machine Learningverified-telegram-icon
40 146
📌 How to Evaluate Retrieval Quality in RAG Pipelines: Precision@k, Recall@k, and F1@k 🗂 Category: LARGE LANGUAGE MODELS 🕒
📌 How to Evaluate Retrieval Quality in RAG Pipelines: Precision@k, Recall@k, and F1@k 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-10-16 | ⏱️ Read time: 18 min read In my previous posts, I have walked you through putting together a very basic RAG…

Machine Learning
Machine Learningverified-telegram-icon
40 146
📌 A Beginner’s Guide to Robotics with Python 🗂 Category: ROBOTICS 🕒 Date: 2025-10-16 | ⏱️ Read time: 9 min read Build 3D s
📌 A Beginner’s Guide to Robotics with Python 🗂 Category: ROBOTICS 🕒 Date: 2025-10-16 | ⏱️ Read time: 9 min read Build 3D simulations with PyBullet

Machine Learning
Machine Learningverified-telegram-icon
40 146
📌 Stop Feeling Lost : How to Master ML System Design 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-10-16 | ⏱️ Read time: 6 min
📌 Stop Feeling Lost :  How to Master ML System Design 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-10-16 | ⏱️ Read time: 6 min read What machine learning system design is and how to prepare for it

Machine Learning
Machine Learningverified-telegram-icon
40 146
📌 Feature Detection, Part 1: Image Derivatives, Gradients, and Sobel Operator 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-10
📌 Feature Detection, Part 1: Image Derivatives, Gradients, and Sobel Operator 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-10-16 | ⏱️ Read time: 11 min read Applying calculus fundamentals to computer vision for edge detection