Machine Learning
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Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.

Admin: @HusseinSheikho || @Hussein_Sheikho
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πŸ“Œ Multi-Agent Arena: Insights from London Great Agent Hack 2025

πŸ—‚ Category: AGENTIC AI

πŸ•’ 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"β€”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
πŸ“Œ How to Code Your Own Website with AI

πŸ—‚ Category: AGENTIC AI

πŸ•’ 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
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πŸ“Œ How to Use Simple Data Contracts in Python for Data Scientists

πŸ—‚ Category: DATA SCIENCE

πŸ•’ 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
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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

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βœ… https://t.iss.one/Codeprogrammer
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πŸ“Œ The Step-by-Step Process of Adding a New Feature to My IOS App with Cursor

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ 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
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πŸ“Œ The Machine Learning β€œAdvent Calendar” Day 5: GMM in Excel

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ 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
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πŸ“Œ A Product Data Scientist’s Take on LinkedIn Games After 500 Days of Play

πŸ—‚ Category: DATA SCIENCE

πŸ•’ 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
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πŸ“Œ YOLOv1 Paper Walkthrough: The Day YOLO First Saw the World

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ 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
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πŸ“Œ On the Challenge of Converting TensorFlow Models to PyTorch

πŸ—‚ Category: DEEP LEARNING

πŸ•’ 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
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πŸ“Œ Do Labels Make AI Blind? Self-Supervision Solves the Age-Old Binding Problem

πŸ—‚ Category: DEEP LEARNING

πŸ•’ 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
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πŸ“Œ The Machine Learning β€œAdvent Calendar” Day 4: k-Means in Excel

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ 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
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πŸ“Œ Build and Deploy Your First Supply Chain App in 20 Minutes

πŸ—‚ Category: PROGRAMMING

πŸ•’ 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
πŸ“Œ Bootstrap a Data Lakehouse in an Afternoon

πŸ—‚ Category: DATA ENGINEERING

πŸ•’ Date: 2025-12-04 | ⏱️ Read time: 12 min read

Using Apache Iceberg on AWS with Athena, Glue/Spark and DuckDB

#DataScience #AI #Python
πŸ“Œ The Best Data Scientists are Always Learning

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2025-12-04 | ⏱️ Read time: 7 min read

Why continuous learning matters & how to come up with topics to study

#DataScience #AI #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.iss.one/CodeProgrammer βœ…
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πŸ“Œ Reading Research Papers in the Age of LLMs

πŸ—‚ Category: LARGE LANGUAGE MODELS

πŸ•’ 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
πŸ€–πŸ§  Supervised Reinforcement Learning: A New Era of Step-Wise Reasoning in AI

πŸ—“οΈ 23 Nov 2025
πŸ“š 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
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πŸ€–πŸ§  CALM: Revolutionizing Large Language Models with Continuous Autoregressive Learning

πŸ—“οΈ 23 Nov 2025
πŸ“š 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
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πŸ€–πŸ§  Agent-o-rama: The End-to-End Platform Transforming LLM Agent Development

πŸ—“οΈ 23 Nov 2025
πŸ“š 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
πŸ€–πŸ§  DeepEyesV2: The Next Leap Toward Agentic Multimodal Intelligence

πŸ—“οΈ 23 Nov 2025
πŸ“š AI News & Trends

The evolution of artificial intelligence has reached a stage where models are no longer limited to understanding text or images independently. The emergence of multimodal AI systems capable of processing and reasoning across multiple types of data has transformed how machines interpret the world. Yet, most existing multimodal models remain passive observers, unable to act ...

#DeepEyesV2 #AgenticMultimodalIntelligence #MultimodalAI #AIEvolution #ActiveReasoning #AIAction