This is where data augmentation comes in: even if you’re starting with very little, you can end up with massive amounts of data to generate insights, predictions, and recommendations that were previously unavailable due to a lack of relevant information. The problem is that most companies don’t have enough data to train their AI models. Further, large neural networks, or deep learning models, need a huge amount of data, so they benefit even more from data augmentation techniques. In fact, research studies have found that basic data augmentation can greatly improve accuracy on image tasks, such as classification and segmentation. Data augmentation is crucial for many AI applications, as accuracy increases with the amount of training data.
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