Hyperparameter Tuning and Neural Architecture Search

Key Hyperparameters to Tune

Some of the most common ones to tune:

• Learning rate: controls how big each training step is. Too high, and training bounces around chaotically. Too low, and it crawls along forever
• Batch size: controls how many examples the model looks at before updating its weights
• Number of layers and neurons: affects how much the network can learn, but also how likely it is to overfit
• Dropout rate: how aggressively you fight overfitting
• Optimizer choice: Adam, SGD, or something else entirely

Grid Search vs Random Search vs Bayesian Optimization

1. Grid Search

Grid search tries every possible combination of predefined hyperparameter values. It is very thorough, but becomes expensive and slow as the number of hyperparameters increases.`

2. Random Search

Random search selects random combinations of hyperparameters instead of testing all possibilities. It is often more efficient than grid search because it can find good configurations faster without exploring every option.

3. Bayesian Optimization

Bayesian optimization uses past results to guide future choices. It builds a model of what works well and uses that information to focus on more promising hyperparameter combinations, reducing wasted trials.

Automated Neural Architecture Search (NAS)

NAS pushes automation even further. Instead of just tuning settings for a network you already designed, it actually searches for the network's structure itself, how many layers, what type each one should be, how they connect.

This sounds like magic, but it works through a similar trial-and-search process as hyperparameter tuning, just applied to architecture decisions. Different candidate architectures get built and tested, and the search gradually favors designs that perform well, often using reinforcement learning or evolutionary algorithms to guide it instead of trying every possible design, which would take forever.

NAS has actually found genuinely clever network designs that a human engineer might never have thought to try. The tradeoff is that it demands serious computing resources to run.