اکنون در دسترس! پژوهش تلگرام ۲۰۲۵ — مهمترین بینشهای سال 
Machine Learning
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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 40 123 مشترک است و جایگاه 3 380 را در دسته فناوری و برنامهها و رتبه 231 را در منطقه سوريا دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 40 123 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 25 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 395 و در ۲۴ ساعت گذشته برابر 12 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 1.89% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.31% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 758 بازدید دریافت میکند. در اولین روز معمولاً 525 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 26 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.
40 123
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40 134
🤖🧠 Agent-o-rama: The End-to-End Platform Transforming LLM Agent Development
🗓️ 23 Nov 2025
📚 AI News & Trends
As large language models (LLMs) become more capable, developers are increasingly using them to build intelligent AI agents that can perform reasoning, automation and decision-making tasks. However, building and managing these agents at scale is far from simple. Challenges such as monitoring model behavior, debugging reasoning paths, testing reliability and tracking performance metrics can make ...
#AgentoRama #LLMAgents #EndToEndPlatform #AIIntelligence #ModelMonitoring #AIDevelopment
40 134
🤖🧠 CALM: Revolutionizing Large Language Models with Continuous Autoregressive Learning
🗓️ 23 Nov 2025
📚 AI News & Trends
Large Language Models (LLMs) such as GPT, Claude and Gemini have dramatically transformed artificial intelligence. From generating natural text to assisting in code and research, these models rely on one fundamental process: autoregressive generation predicting text one token at a time. However, this sequential nature poses a critical efficiency bottleneck. Generating text token by token ...
#CALM #ContinuousAutoregressiveLearning #LargeLanguageModels #AutoregressiveGeneration #AIEfficiency #AIInnovation
40 134
🤖🧠 Supervised Reinforcement Learning: A New Era of Step-Wise Reasoning in AI
🗓️ 23 Nov 2025
📚 AI News & Trends
In the evolving landscape of artificial intelligence, large language models (LLMs) like GPT, Claude and Qwen have demonstrated remarkable abilities from generating human-like text to solving complex problems in mathematics, coding, and logic. Yet, despite their success, these models often struggle with multi-step reasoning, especially when each step depends critically on the previous one. Traditional ...
#SupervisedReinforcementLearning #StepWiseReasoning #ArtificialIntelligence #LargeLanguageModels #MultiStepReasoning #AIBreakthrough
40 134
📌 Reading Research Papers in the Age of LLMs
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-12-06 | ⏱️ Read time: 10 min read
How I keep up with papers with a mix of manual and AI-assisted reading
#DataScience #AI #Python
40 134
Repost from Machine Learning with Python
If you want to truly understand how AI systems like #GPT, #Claude, #Llama or #Mistral work at their core, these 85 foundational concepts are essential. The visual below breaks down the most important ideas across the full #AI and #LLM landscape.
https://t.me/CodeProgrammer ✅
40 134
📌 The Best Data Scientists are Always Learning
🗂 Category: DATA SCIENCE
🕒 Date: 2025-12-04 | ⏱️ Read time: 7 min read
Why continuous learning matters & how to come up with topics to study
#DataScience #AI #Python
40 134
📌 Bootstrap a Data Lakehouse in an Afternoon
🗂 Category: DATA ENGINEERING
🕒 Date: 2025-12-04 | ⏱️ Read time: 12 min read
Using Apache Iceberg on AWS with Athena, Glue/Spark and DuckDB
#DataScience #AI #Python
40 134
📌 Build and Deploy Your First Supply Chain App in 20 Minutes
🗂 Category: PROGRAMMING
🕒 Date: 2025-12-04 | ⏱️ Read time: 21 min read
A factory operator that discovered happiness by switching from notebook to streamlit – (Image Generated…
#DataScience #AI #Python
40 134
📌 The Machine Learning “Advent Calendar” Day 4: k-Means in Excel
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-04 | ⏱️ Read time: 7 min read
Discover how to implement the k-Means clustering algorithm, a fundamental machine learning technique, using only Microsoft Excel. This guide, part of a "Machine Learning Advent Calendar" series, walks through building a training algorithm from scratch in a familiar spreadsheet environment, demystifying what "real" ML looks like in practice.
#MachineLearning #kMeans #Excel #DataScience #Tutorial
40 134
📌 Do Labels Make AI Blind? Self-Supervision Solves the Age-Old Binding Problem
🗂 Category: DEEP LEARNING
🕒 Date: 2025-12-04 | ⏱️ Read time: 16 min read
A new NeurIPS 2025 paper suggests that traditional labels may hinder an AI's holistic image understanding, a challenge known as the "binding problem." Research shows that self-supervised learning methods can overcome this, significantly improving the capabilities of Vision Transformers (ViT) by allowing them to better integrate various visual features without explicit labels. This breakthrough points to a future where models learn more like humans, leading to more robust and nuanced computer vision.
#AI #SelfSupervisedLearning #ComputerVision #ViT
40 134
📌 On the Challenge of Converting TensorFlow Models to PyTorch
🗂 Category: DEEP LEARNING
🕒 Date: 2025-12-05 | ⏱️ Read time: 19 min read
Converting legacy TensorFlow models to PyTorch presents significant challenges but offers opportunities for modernization and optimization. This guide explores the common hurdles in the migration process, from architectural differences to API incompatibilities, and provides practical strategies for successfully upgrading your AI/ML pipelines. Learn how to not only convert but also enhance your models for better performance and maintainability in the PyTorch ecosystem.
#PyTorch #TensorFlow #ModelConversion #MLOps #DeepLearning
40 134
📌 YOLOv1 Paper Walkthrough: The Day YOLO First Saw the World
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2025-12-05 | ⏱️ Read time: 17 min read
A deep dive into the original YOLOv1 paper, exploring the revolutionary "You Only Look Once" algorithm. This technical walkthrough breaks down the foundational object detection architecture and guides readers through a complete implementation from scratch using PyTorch. It's an essential resource for understanding the core mechanics of single-shot detectors and the history of computer vision.
#YOLO #ObjectDetection #ComputerVision #PyTorch
40 134
📌 A Product Data Scientist’s Take on LinkedIn Games After 500 Days of Play
🗂 Category: DATA SCIENCE
🕒 Date: 2025-12-05 | ⏱️ Read time: 14 min read
A product data scientist offers a unique analysis of LinkedIn's puzzle games after 500 consecutive days of play. The article delves into key takeaways on user engagement, experimentation, and data-driven product thinking, revealing how observing a simple game can provide valuable lessons for complex product strategy and development in the tech industry.
#DataScience #ProductManagement #UserEngagement #Experimentation
40 134
📌 The Machine Learning “Advent Calendar” Day 5: GMM in Excel
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-05 | ⏱️ Read time: 6 min read
Explore Gaussian Mixture Models (GMM), a powerful clustering algorithm that serves as a natural extension and improvement over k-Means. This guide, part of a Machine Learning Advent Calendar series, uniquely demonstrates how to implement and understand GMMs entirely within Microsoft Excel. It's a practical approach for grasping core ML concepts without requiring a dedicated coding environment, making advanced data science techniques more accessible.
#MachineLearning #GMM #Excel #DataScience #Clustering
40 134
📌 The Step-by-Step Process of Adding a New Feature to My IOS App with Cursor
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2025-12-05 | ⏱️ Read time: 6 min read
This article provides a step-by-step walkthrough of adding a new feature to an iOS app using the AI-powered editor, Cursor. It offers practical insights for developers, highlighting how Cursor excels at code generation but is less effective for UI/UX design tasks. This guide demonstrates a real-world workflow for integrating AI coding assistants into the development process, showcasing both their strengths and limitations.
#iOSDev #AICoding #AppDevelopment #Cursor
40 134
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://t.me/addlist/8_rRW2scgfRhOTc0
✅ https://t.me/Codeprogrammer
40 134
📌 How to Use Simple Data Contracts in Python for Data Scientists
🗂 Category: DATA SCIENCE
🕒 Date: 2025-12-02 | ⏱️ Read time: 5 min read
Prevent your data pipelines from breaking unexpectedly. This article demonstrates how to implement simple data contracts in Python using Pandera, an open-source validation library. Learn to define and enforce data quality rules to build more robust and reliable data science workflows, ensuring your data meets expectations before it causes issues downstream.
#DataContracts #Python #DataScience #Pandera #DataValidation
40 134
📌 How to Code Your Own Website with AI
🗂 Category: AGENTIC AI
🕒 Date: 2025-12-03 | ⏱️ Read time: 8 min read
Unlock the potential of AI in web development. This guide explains how to "vibe-code" a website, a modern method where AI tools translate your high-level design concepts and desired feel directly into functional code. Learn a more intuitive and streamlined approach to building websites from scratch.
#AI #WebDevelopment #AICoding #DeveloperTools #GenerativeAI
40 134
📌 Multi-Agent Arena: Insights from London Great Agent Hack 2025
🗂 Category: AGENTIC AI
🕒 Date: 2025-12-03 | ⏱️ Read time: 16 min read
Key insights from the London Great Agent Hack 2025 reveal critical success factors for multi-agent systems. The focus has shifted towards building highly robust agents capable of withstanding adversarial testing and unexpected scenarios. A major theme was the importance of "glass-box reasoning"—making agent decision-making transparent and interpretable. Ultimately, red-team resilience and explainability, not just raw performance, were the defining characteristics of the top-performing solutions.
#MultiAgentSystems #AIAgents #ExplainableAI #AISecurity
40 134
📌 How to Turn Your LLM Prototype into a Production-Ready System
🗂 Category: LLM APPLICATIONS
🕒 Date: 2025-12-03 | ⏱️ Read time: 15 min read
Transforming a promising LLM prototype into a production-ready system involves significant engineering challenges. This guide outlines the essential steps and best practices for moving beyond the experimental phase, focusing on building scalable, reliable, and efficient LLM applications for real-world deployment. Learn how to successfully operationalize your language model from concept to production.
#LLM #MLOps #ProductionAI #LLMOps
