Summary – Hyperparameters in Neural Networks

Basic Observations

Hyperparameter Effect
Epochs More epochs = better accuracy up to a point. Too much = overfitting risk.
Learning Rate Too high = weight jumps and may never converge. Too low = slow learning.
Loss Shows how well the model is learning with each epoch.

Try changing:

  • learning_rate = 0.5 → Fast but unstable.
  • learning_rate = 0.0001 → Very slow learning.
  1. Hyperparameters are not learned from data.
  2. Tuning them is essential for good model performance.
  3. Manual tuning, Grid Search, or Bayesian Optimization are used in real systems.

Hyperparameters in Neural Networks – Visual Roadmap