A machine learning primer written specifically for software engineers who want to build intuition for ML systems using familiar engineering mental models. Organized in three parts covering fundamentals (neurons, backprop, generalization), architectures (transformers, convolution, attention, diffusion, RL), and gates as control
Table of contents
🎯 Who This Is For💡 What Makes This Different📐 What It Covers📖 Read It🧭 How to Use This🖼️ Visualizations📝 Origin🤝 Contributing📄 LicenseSort: