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Artificial Intelligence and machine learning have come a long way since their inception in the late 1950s. This article explains the main differences between neural networks and deep learning. Neural networks are made up of machine learning algorithms and focus on discovering underlying patterns in a dataset, while deep learning is a subfield of machine learning that uses multiple layers of nonlinear processing units to transform and extract features. The parts of a neural network include neurons, connections and weights, and backpropagation.
