Tuning Hyperparameters in Neural Networks

Understanding hyperparameter tuning in neural networks is essential for enhancing model performance. Key hyperparameters include learning rate, batch size, number of epochs, activation function, and dropout. Techniques for tuning these parameters include manual search, grid search, and random search. These methods help find optimal settings and improve the model's accuracy and efficiency.