Hyperparameter

A hyperparameter is an external parameter set in advance to control the training process of a machine learning or deep learning model. It has a significant impact on model performance, convergence speed, and generalization ability, making it a critically important tuning variable in AI development. Unlike model parameters (e.g., weights and biases), which are optimized automatically during training, hyperparameters must be set by a human beforehand—this is the key distinction. Examples of common hyperparameters: • Learning rate: Controls the size of each parameter update step • Number of epochs: How many times the training data is cycled through during training • Batch size: The number of data samples used per training step • Number of hidden layers and nodes (network architecture) • Dropout rate: The proportion of nodes deactivated to prevent overfitting • Regularization coefficient (L1/L2, etc.): Suppresses model complexity • Gradient optimization algorithm (e.g., Adam, SGD, etc.) Selecting appropriate hyperparameters is essential for improving model performance. Hyperparameter tuning is considered one of the most important steps in AI development. Common tuning approaches include: • Grid search: Trying all combinations within a predefined range • Random search: Randomly sampling parameter values to explore • Bayesian optimization: Efficiently finding optimal values with fewer trials • Automated machine learning (AutoML): An algorithm that automatically performs the optimization Hyperparameters can be thought of as the "blueprint" that defines the rules and environment for AI model training. Because the same algorithm can yield vastly different results depending on how they are set, they are a key factor directly tied to model accuracy improvement.