# Introduction Stochastic Gradient Descent is a popular optimization technique used for minimizing a loss function. The technique performs gradient descent step by step in a stochastic manner, i.e., by randomly selecting samples for each iteration. The method is efficient, especially for fitting linear models. However, convergence is not guaranteed at each iteration, and the loss function may not necessarily decrease at each iteration. In this case, monitoring the convergence on the loss function can be difficult. In this lab, we will explore the early stopping strategy, which is an approach for monitoring convergence on a validation score. We will use the `SGDClassifier` model from the scikit-learn library and the MNIST dataset to illustrate how early stopping can be used to achieve almost the same accuracy as compared to a model built without early stopping, and significantly reduce training time. ## 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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