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A neural network is a collection of connected input/output units with weights assigned to each connection.
Labeled data is used to train classifier algorithms; in the case of image recognition, for instance, the classifier is given training data that includes labels for the images. After sufficient training, the classifier then can receive unlabeled images as inputs and will output classification labels for each image. A classifier is a system that allows you to input data and then receive results that are connected to the classification (or grouping) to which those inputs belong. The iris dataset is a typical dataset to test classifiers with as an illustration.
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