Step-by-Step Guide to Bayesian Optimization: A Python-based Approach

<p>&nbsp;consider a function&nbsp;<em>f</em>&nbsp;that is inaccessible to us. We cannot directly access&nbsp;<em>f</em>&nbsp;or compute its gradients. Our only available information is providing an input&nbsp;<em>x</em>&nbsp;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&nbsp;<em>f</em>&nbsp;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> <p><a href="https://medium.com/@okanyenigun/step-by-step-guide-to-bayesian-optimization-a-python-based-approach-3558985c6818"><strong>Visit Now</strong></a></p>