Description: Gaussian noise is a type of statistical noise characterized by having a probability density function that follows the normal distribution, also known as Gaussian distribution. This type of noise is fundamental in the field of neural networks, as it is used to model uncertainty and variability in data. In the context of neural networks, Gaussian noise can be introduced into inputs or hidden layers to enhance model robustness and prevent overfitting. Additionally, its random nature allows for simulating different scenarios and conditions, which is especially useful in training deep learning models. In convolutional neural networks, Gaussian noise can help improve generalization by forcing the model to learn more relevant features that are less dependent on specific training data. In generative adversarial networks (GANs), it is used to generate images and synthetic data that mimic the distribution of real data. Finally, in recurrent neural networks (RNNs), Gaussian noise can be useful for modeling time series with inherent variability, allowing the model to learn more complex and robust patterns.
