Neural network tuning, often referred to as hyperparameter optimization, involves adjusting the network's hyperparameters to improve its performance. Hyperparameters are the settings or configurations that govern the overall behavior of a neural network but are not learned from the data.
The Process of Neural Network Tuning
Tuning a neural network typically involves experimenting with various hyperparameters like learning rate, number of layers, number of neurons in each layer, activation functions, and more. The goal is to find the best combination that minimizes a predefined loss function on a given dataset. Techniques like grid search, random search, and Bayesian optimization are commonly used for this process. Proper tuning can significantly enhance the performance of a neural network on specific tasks.
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