# Introduction In this lab, we will demonstrate how to use Stochastic Gradient Descent (SGD) to approximate the solution of a One-Class SVM in the case of an RBF kernel. We will compare the results of this approximation to the results of using a One-Class SVM with a kernelized approach. The purpose of this lab is not to show the benefits of approximation in terms of computation time, but rather to demonstrate that similar results can be obtained using SGD on a toy dataset. ## VM Tips After the VM startup is done, click the top left corner to switch to the **Notebook** tab to access Jupyter Notebook for practice. Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook. If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.
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