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When the data is small, deep learning algorithms don�t perform that well. The artificial neural networks using deep learning send the input (the data of images) through different layers of the network, with each network hierarchically defining specific features of images. The deep learning architecture is flexible to be adapted to new problems in the future. Taking steps to reduce the skill shortage in deep learning domain.
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Prevent bias in deep learning with multiple approaches like introducing more diversity in the field; These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing it to “learn” from large amounts of data. If your data are images, create randomly modified versions of existing images. This is because deep learning algorithms need a large amount of data to understand it. A theory that goes beyond describing what individual neurons do, and explains the emergent behavior of the entire network. Its ability to learn unsupervised drives continuous improvement in accuracy and outcomes.
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