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📰 Dev.to · shangkyu shin

Articles from Dev.to · shangkyu shin · 17 articles · Updated every 3 hours · View all reads

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CNNs Explained: How Image Classification Actually Works in Deep Learning
Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 2d ago
CNNs Explained: How Image Classification Actually Works in Deep Learning
Understanding CNNs means understanding how models turn raw pixels into structured representations....
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Neural Network Optimization Challenges — Fixing Vanishing Gradients with Better Architecture Design
Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 2d ago
Neural Network Optimization Challenges — Fixing Vanishing Gradients with Better Architecture Design
Vanishing gradients are one of the main reasons deep neural networks fail. If your deeper model...
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How Neural Networks Actually Learn: Backpropagation, Gradients, and Training Loop (Developer Guide)
Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 2d ago
How Neural Networks Actually Learn: Backpropagation, Gradients, and Training Loop (Developer Guide)
Learn how neural networks train using forward propagation, loss functions, and backpropagation. This...
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Multilayer Perceptron (MLP) — How Neural Networks Learn Representations, Probabilities, and Gradients
Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 2d ago
Multilayer Perceptron (MLP) — How Neural Networks Learn Representations, Probabilities, and Gradients
Multilayer Perceptron (MLP) is the simplest neural network worth learning deeply. It looks basic,...
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Regularization in Machine Learning — How to Actually Prevent Overfitting (L1, L2, Dropout)
Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 3d ago
Regularization in Machine Learning — How to Actually Prevent Overfitting (L1, L2, Dropout)
What is regularization in machine learning, and how do you actually prevent overfitting in practice?...
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Optimization in Machine Learning — How Models Learn Parameters and What Actually Improves Training
Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 3d ago
Optimization in Machine Learning — How Models Learn Parameters and What Actually Improves Training
Learn how optimization in machine learning works, from parameter learning and loss minimization to...
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Optimization vs Regularization — The Real Reason Your Model Overfits (and How to Fix It)
Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 3d ago
Optimization vs Regularization — The Real Reason Your Model Overfits (and How to Fix It)
Most deep learning problems are not architecture problems. They are training...
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Theoretical Foundations of Deep Learning (Why Neural Networks Actually Work)
Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 3d ago
Theoretical Foundations of Deep Learning (Why Neural Networks Actually Work)
Deep learning and neural networks work because of entropy, KL divergence, probability distributions,...
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Fundamentals of Neural Networks: How Simple Math Scales into Modern AI
Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 3d ago
Fundamentals of Neural Networks: How Simple Math Scales into Modern AI
Neural networks power modern AI—from image recognition to large language models. This guide breaks...
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Linear Models in Machine Learning: Why They Still Matter (Regression, Classification, Logistic Regression)
Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 3d ago
Linear Models in Machine Learning: Why They Still Matter (Regression, Classification, Logistic Regression)
Linear models in machine learning are the foundation of regression, classification, and logistic...
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Model Complexity and Generalization: How to Actually Fix Overfitting
Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 3d ago
Model Complexity and Generalization: How to Actually Fix Overfitting
If you've ever trained a model that looked perfect during training but failed in production, you've...
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Machine Learning Tasks and Evaluation: How to Choose the Right Metrics and Avoid Common Pitfalls
Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 3d ago
Machine Learning Tasks and Evaluation: How to Choose the Right Metrics and Avoid Common Pitfalls
Understand how different machine learning tasks require different evaluation strategies. Learn how to...
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What Machine Learning Really Means: From Rules to Data-Driven Systems
Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 3d ago
What Machine Learning Really Means: From Rules to Data-Driven Systems
Machine learning is the foundation of modern AI systems. Learn how models improve from data, optimize...
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Relationship Between Deep Learning and AI Explained
Dev.to · shangkyu shin 🧠 Large Language Models ⚡ AI Lesson 3d ago
Relationship Between Deep Learning and AI Explained
Understanding AI can feel confusing. Where does Deep Learning fit? Is it the same as Machine...
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Concept of Artificial Intelligence: Rational Decision Making and Expected Utility Explained
Dev.to · shangkyu shin 🤖 AI Agents & Automation ⚡ AI Lesson 3d ago
Concept of Artificial Intelligence: Rational Decision Making and Expected Utility Explained
Artificial Intelligence is often explained as machines that “think like humans.” That’s not...
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Traditional Machine Learning in Practice: Learning Paradigms, Algorithm Families, and Evaluation Perspectives
Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 3d ago
Traditional Machine Learning in Practice: Learning Paradigms, Algorithm Families, and Evaluation Perspectives
Traditional machine learning is more than just algorithms. This guide explains how learning...
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Neural Network Learning Systems and Deep Learning: From Perceptrons to Representation Learning
Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 3d ago
Neural Network Learning Systems and Deep Learning: From Perceptrons to Representation Learning
Deep learning did not appear out of nowhere. It grew from a simple question: can a machine learn...
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