Image Augmentation for Classification
Image augmentation in machine learning (ML) is a stable diet for increasing prediction accuracy, especially for the image classification domain. The causality logic is linear, meaning the more robust the data input, the higher the forecast accuracy.
Deep learning (DL) is a subset of ML that uses artificial neural networks to learn patterns and forecast based on the input data. Unlike traditional ML algorithms, which depend on programmer coding and rules to analyze data, DL algorithms automatically learn, solve, and categorize the relationship between data and labels. Thus, expanding the datasets directly impacts DL predictions on new insights that the model has not seen in the training data.
DL algorithms are designed to mimic the human brain, with layers of neurons that process information and pass it on to the next layer. Each layer of neurons learns to extract increasingly complex features from the input data, allowing the network to identify...