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Articles from Dev.to · shangkyu shin · 27 articles · Updated every 3 hours · View all reads

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CNN Layer Composition — A Practical Developer Guide to Activation, Pooling, and Fully Connected Layers

Dev.to · shangkyu shin 1d ago

CNN Layer Composition — A Practical Developer Guide to Activation, Pooling, and Fully Connected Layers

CNNs are not just convolution stacks. This guide explains how activation, pooling, and fully...

CNN Spatial Behavior Explained: Convolution, Stride, Padding, and Output Size (With Intuition)

Dev.to · shangkyu shin 1d ago

CNN Spatial Behavior Explained: Convolution, Stride, Padding, and Output Size (With Intuition)

Understanding CNNs requires more than just architectures. Learn how convolution, stride, padding, and...

CNNs Explained: How Image Classification Actually Works in Deep Learning

Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 1d ago

CNNs Explained: How Image Classification Actually Works in Deep Learning

Understanding CNNs means understanding how models turn raw pixels into structured representations....

Neural Network Optimization Challenges — Fixing Vanishing Gradients with Better Architecture Design

Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 1d 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...

How Neural Networks Actually Learn: Backpropagation, Gradients, and Training Loop (Developer Guide)

Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 1d 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...

Multilayer Perceptron (MLP) — How Neural Networks Learn Representations, Probabilities, and Gradients

Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 1d 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,...

Regularization in Machine Learning — How to Actually Prevent Overfitting (L1, L2, Dropout)

Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 1d 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?...

Optimization in Machine Learning — How Models Learn Parameters and What Actually Improves Training

Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 1d 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...

Optimization vs Regularization — The Real Reason Your Model Overfits (and How to Fix It)

Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 1d 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...

Theoretical Foundations of Deep Learning (Why Neural Networks Actually Work)

Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 1d ago

Theoretical Foundations of Deep Learning (Why Neural Networks Actually Work)

Deep learning and neural networks work because of entropy, KL divergence, probability distributions,...

Fundamentals of Neural Networks: How Simple Math Scales into Modern AI

Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 1d 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...

Linear Models in Machine Learning: Why They Still Matter (Regression, Classification, Logistic Regression)

Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 1d 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...

Model Complexity and Generalization: How to Actually Fix Overfitting

Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 1d 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...

Machine Learning Tasks and Evaluation: How to Choose the Right Metrics and Avoid Common Pitfalls

Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 1d 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...

What Machine Learning Really Means: From Rules to Data-Driven Systems

Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 1d 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...

Relationship Between Deep Learning and AI Explained

Dev.to · shangkyu shin 🧠 Large Language Models ⚡ AI Lesson 1d ago

Relationship Between Deep Learning and AI Explained

Understanding AI can feel confusing. Where does Deep Learning fit? Is it the same as Machine...

Concept of Artificial Intelligence: Rational Decision Making and Expected Utility Explained

Dev.to · shangkyu shin 🤖 AI Agents & Automation ⚡ AI Lesson 1d 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...

Traditional Machine Learning in Practice: Learning Paradigms, Algorithm Families, and Evaluation Perspectives

Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 1d 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...

Neural Network Learning Systems and Deep Learning: From Perceptrons to Representation Learning

Dev.to · shangkyu shin 📐 ML Fundamentals ⚡ AI Lesson 1d 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...

Probabilistic Reasoning in AI: How Bayesian Networks Help AI Think Under Uncertainty

Dev.to · shangkyu shin 1d ago

Probabilistic Reasoning in AI: How Bayesian Networks Help AI Think Under Uncertainty

Real-world AI is messy. Data is noisy, incomplete, and uncertain—and rule-based logic breaks fast in...

Logical Reasoning Systems in AI: How AI Represents Knowledge, Uses First-Order Logic, and Reasons Step by Step

Dev.to · shangkyu shin 1d ago

Logical Reasoning Systems in AI: How AI Represents Knowledge, Uses First-Order Logic, and Reasons Step by Step

Most AI today is about deep learning. But what if systems could reason, not just predict? This post...

Search-Based Problem Solving in AI: State Space, Search Trees, Heuristics, A*, Local Search, and Game Search

Dev.to · shangkyu shin 1d ago

Search-Based Problem Solving in AI: State Space, Search Trees, Heuristics, A*, Local Search, and Game Search

Cross-posted from Zeromath. Original article:...

Thinking Machines and Human Questions: Turing Test, Chinese Room, Strong AI, and the Future of Intelligence

Dev.to · shangkyu shin 1d ago

Thinking Machines and Human Questions: Turing Test, Chinese Room, Strong AI, and the Future of Intelligence

Cross-posted from Zeromath. Original article: https://zeromathai.com/en/thinking-machine-en/ AI used...

AI Paradigms: From Symbolic Rules to Neural Networks and Intelligent Agents

Dev.to · shangkyu shin 1d ago

AI Paradigms: From Symbolic Rules to Neural Networks and Intelligent Agents

Cross-posted from Zeromath. Original article:...