Fine-tuning the Model
Fine-tuning a neural network involves optimizing its performance by adjusting various aspects of the model. In this chapter, we will explore techniques for fine-tuning a neural network:
1. Hyperparameter Tuning:
Hyperparameters are settings that determine the behavior of the neural network but are not learned from the data.
Examples of hyperparameters include learning rate, batch size, number of hidden layers, number of neurons in each layer, regularization parameters, and activation functions.
Fine-tuning involves systematically varying these hyperparameters and evaluating the networks performance to find the optimal configuration.
2. Learning Rate Scheduling:
The learning rate controls the step size in parameter updates during training.
Choosing an appropriate learning rate is crucial for convergence and preventing overshooting or getting stuck in local minima.
Learning rate scheduling techniques, such as reducing the learning rate over time or using adaptive methods like Adam or RMSprop, can help fine-tune the models performance.