Step-by-Step Guide to Bayesian Optimization: A Python-based Approach
<p> consider a function <em>f</em> that is inaccessible to us. We cannot directly access <em>f</em> or compute its gradients. Our only available information is providing an input <em>x</em> and receiving a noisy estimation (or without any noise) of the true output.</p>
<p><img alt="" src="https://miro.medium.com/v2/resize:fit:291/1*0itUMUb3xCIRbEb9EZoelg.png" style="height:65px; width:291px" /></p>
<p>Our objective is to optimize the underlying value within the black box function <em>f</em> according to our purpose, maximizing it for example.</p>
<p>In traditional optimization methods, such as grid search or random search, the objective function is evaluated at a predefined set of points. However, these methods can be computationally expensive and inefficient when dealing with high-dimensional or noisy functions.</p>
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