What is data augmentation in neural network

This recipe explains what is data augmentation in neural network

Recipe Objective - What is Data Augmentation?

Data augmentation is the approach of generating more training data from the existing training samples by the samples through a number of random augmenting transformations that yield believable looking data(images in image dataset). The goal is that at the training time, the model will never see the exact same data or feature(picture) twice. This helps expose the model to more aspects of the data and generalize better on the data as it proves better for the neural network. Also, given infinite data, the model would be exposed to every possible aspect of data distribution at hand so, the model will never overfit.

Learn to Build a Multi Class Image Classification Model in Python from Scratch

Explanation of Data augmentation.

When a neural network is trained using data augmentation configuration, the neural network will never see the same input twice. But the inputs it sees are still heavily intercorrelated with each other because they come from a small number of the original images. Further more, dropout layer can be added in the network to avoid overfitting.

Data augmentation improves the accuracy of the model and further using regularization techniques and by hyperparameter tuning , a much improvement in accuracy can be achieved.

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