Question: Explain the concept of data augmentation in image classification using TensorFlow.

Answer: Data augmentation involves applying random transformations to input data during training to increase the diversity of the dataset. In image classification, this can include rotations, flips, and zooms.

Example:

data_augmentation = tf.keras.Sequential([
tf.keras.layers.experimental.preprocessing.RandomFlip('horizontal'),
tf.keras.layers.experimental.preprocessing.RandomRotation(0.2),
])

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