hyperparameter tuning in neural networks
Hyperparameter tuning in neural networks refers to the process of finding the optimal configuration for the hyperparameters of a neural network model. Hyperparameters are settings that are determined prior to the training of the model and control various aspects of the learning process. Hyperparameter tuning involves systematically exploring different combinations of hyperparameters to maximize the performance and generalization of the neural network model. By adjusting hyperparameters such as learning rate, batch size, number of hidden layers, and activation functions, the network's ability to learn and generalize from the data can be optimized. The goal of hyperparameter tuning is to find the optimal settings that result in the best possible performance of the neural network on the given task or dataset.
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