Supervised machine learning uses labeled data to train models for classification or regression over a set of targets. The performance of a model is a function of the data that is used to train it. The less data that is available, the harder it is for a model to learn to make accurate predictions on unseen data.
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Convolution Neural Networks (CNN) learns image regognition the way human visual system does. It scans images by using filters which recognizes a unique feature. A little deeper layers identify low level features such as curves and edges, while the deeper layers idtentifies high level features such as eyes or windows. We use Keras library to visualize what CNN are learning to look when making a certain classfication.
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