Optimization Algorithm
An optimization algorithm is a procedure that is used to find the best solution or set of solutions among a large set of possible solutions to a given problem. In data science and artificial intelligence, optimization algorithms are used to find the optimal values of parameters in a model that minimize or maximize a given objective function. These algorithms can be classified into two main categories: deterministic and stochastic. Deterministic algorithms, such as gradient descent and Newton's method, use a fixed set of rules to find the optimal solution. Stochastic algorithms, such as genetic algorithms and simulated annealing, use randomization to explore the search space and find the optimal solution. Optimization algorithms are widely used in machine learning, deep learning, and other data science applications to train models and improve their performance.
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