Procedures that search for the best answer or learn it from examples by improving a candidate step by step. Gradient descent and backpropagation adjust the parameters of a model to reduce its error, k-means clustering groups observations around central points, and genetic algorithms and simulated annealing borrow ideas from natural selection and from the cooling of metal to search large spaces of possible solutions for a good one.
All Optimization and Machine Learning Algorithms
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Backpropagation, Wikipedia
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In machine learning, backpropagation is a gradient computation method commonly used for training a neural network in computing parameter updates.
Wikipedia: Gradient Descent
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Gradient descent is a method for unconstrained mathematical optimization.
Wikipedia: K-means clustering
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k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid).
Wikipedia: Genetic algorithm
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A genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA) in computer science and operations research.
Wikipedia: Simulated annealing
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Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function.
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