Hyperparameters are configuration settings that control the learning process. Unlike model parameters (weights), they must be set before training begins.
📊 Learning Rate
Typical range: 1e-5 to 1.0
📦 Batch Size
Typical range: 16 to 512
🏗️ Network Architecture
Layers: 2-100+, Neurons: 16-1024+
🛡️ Regularization
Dropout: 0-0.8, L2: 1e-6 to 0.1
Tuning Strategies
🔧 Manual Tuning
Systematic trial-and-error based on intuition and experience
📏 Grid Search
Exhaustive search over specified parameter grid
🎲 Random Search
Sample random combinations from parameter distributions
🧠 Bayesian Optimization
Smart search using probabilistic model of objective function