Implementing Stochastic Gradient Descent

# Introduction

Stochastic Gradient Descent (SGD) is a popular optimization algorithm used in machine learning. It is a variation of the gradient descent algorithm that uses a randomly selected subset of the training data at each iteration. This makes it computationally efficient and suitable for handling large datasets. In this lab, we will walk through the steps of implementing SGD in Python using scikit-learn.

## 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.