What is data augmentation in TensorFlow.js?
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What is data augmentation in TensorFlow.js?
Data augmentation in TensorFlow.js refers to techniques used to artificially expand the size of a training dataset by applying various transformations to the existing data.
Can you give me some examples of data augmentation techniques?
Sure! Common techniques include rotation, flipping, scaling, and color adjustments. These help improve model robustness.
How do I implement data augmentation in my TensorFlow.js project?
You can use the tf.image module to apply transformations. For example, tf.image.flipLeftRight(image) flips an image horizontally.
Are there any libraries that can help with this?
Yes, libraries like tfjs-augment provide pre-built functions for various augmentation techniques, making it easier to integrate.
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