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Data augmentation is a technique used in deep learning to improve the performance of a machine learning model by increasing the amount of training data. This is done by artificially generating new data from the existing training data through a series of transformations. These transformations can include things like cropping, rotating, flipping, and adding noise to the original data.

Data augmentation is particularly useful for deep learning because it allows the model to generalize better to new data and can help reduce overfitting. It is also useful when the amount of available training data is limited, as it allows the model to learn from a larger, artificially expanded dataset.

In this video, we will explore the concept of data augmentation in more detail and demonstrate how it can be implemented in Python using the popular deep learning library, Keras. We will also discuss some best practices for data augmentation and how to effectively use it to improve the performance of your machine learning models.

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