Deep learning is concerned with improving the process by which machines learn new things. A data scientist determines the rules and data set features to include in models with rule-based AI and ML, which drives how those models operate. The data scientist uses deep learning to feed raw data into an algorithm. The system then analyses the data without any preprogrammed rules or features. Once the system has made its predictions, they are validated against a separate set of data. The accuracy (or lack thereof) of these predictions then influences the system's next set of predictions. The "deep" in Deep Learning refers to the many layers accumulated by the neural network over time, with performance improving as the network becomes deeper.
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