Hyperparameter Tuning

Optimizing model performance by finding the best hyperparameters

Hyperparameters are like the settings on your oven - you need to find the right temperature and time to bake the perfect cake! In AI, hyperparameters are settings like learning rate, batch size, and number of layers. Tuning them means testing different combinations to find what works best for your model.

What are Hyperparameters?

Hyperparameters are configuration settings used to control the learning process, set before training begins. Unlike model parameters (weights), hyperparameters are not learned from data. Finding optimal values significantly impacts model performance.

Common Hyperparameters:

  • • Learning rate - How fast the model learns
  • • Batch size - Number of samples per training iteration
  • • Number of epochs - How many times to train on full dataset
  • • Network architecture - Number and size of layers
  • • Dropout rate - Regularization strength
  • • Optimizer - Adam, SGD, RMSprop

Tuning Techniques

Grid Search

Try all combinations of predefined values. Thorough but computationally expensive.

Random Search

Sample random combinations. Often more efficient than grid search.

Bayesian Optimization

Use probabilistic models to guide search. Smart and efficient.

Genetic Algorithms

Evolve hyperparameters using evolutionary principles.

Interview Tips

  • 💡Distinguish hyperparameters (set before training) vs parameters (learned during training)
  • 💡Know common techniques: grid search, random search, Bayesian optimization
  • 💡Understand learning rate is often the most important hyperparameter
  • 💡Be aware of overfitting to validation set during extensive tuning
  • 💡Know tools: Optuna, Ray Tune, Hyperopt, scikit-learn GridSearchCV