Summary

This chapter gave you an overview of the theory underpinning machine learning algorithms, looking at constructing a loss function, and using gradient descent. These fundamental concepts will help you better understand a lot of the implementation details of current deep learning practices. They will also help you separate yourself from your peers. The exercises in this chapter focused on hands-on practical skills such as building and training machine learning algorithms for AI from scratch. With the practical skills you learned from this chapter, you will be able to build machine learning models to solve real-world problems.

We started by training a simple linear regression model and implementing a gradient descent algorithm using NumPy from scratch, which helped us better understand how to train a machine learning model. Then we moved on to building training models with PyTorch low-level modules. We also talk about batch gradient descent versus mini-batch SGD in depth. We...