Deep Learning with R Cookbook

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Overfitting is a common challenge in scenarios where we have only a few data samples to learn from. This prevents our model from performing robustly on unseen data. There are a few techniques that help us deal with this issue:

Data augmentation: It is a technique that reduces overfitting by generating more training data from existing samples in the data and augmenting the samples via several random transformations that produce believable-looking images. It creates modified versions by applying operations like shifting, flipping, zooming, and so on. It also enriches our data, which helps to generalize our model and make it more robust. Data augmentation is done only on the training set.

The keras library in R provides ...