Gaussian Processes
Bayesian Optimization explained with applications in Hyperparameter Optimization and Neural Architecture Search. Uses Gaussian Processes to balance exploration and exploitation, adapting iteratively for dynamic input space exploration. Versatile optimization technique with evolving applications.
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Bayesian Optimization, Part 1 introduces the concept of BayesOpt and its applications in various fields like Hyperparameter Optimization, Neural Architecture Search, and more. The article explains the need for BayesOpt in scenarios where the objective function is unknown, noisy, and expensive to evaluate. It highlights the practical challenges faced in optimization tasks, using the example of baking the perfect chocolate chip cookie to illustrate the concept. The key idea behind BayesOpt lies in iteratively proposing inputs based on a surrogate model fitted to past evaluations and balancing exploration and exploitation using an acquisition function. The use of Gaussian Processes to model uncertainty and guide the optimization process is also discussed. The article emphasizes the iterative nature of BayesOpt, where the acquisition function adapts based on new evaluations, leading to a dynamic exploration of the input space. Despite challenges in defining a simple stopping criterion, BayesOpt remains a versatile and evolving optimization technique with applications in diverse problem domains.
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