Summary

In this chapter, we covered essential knowledge for building and training deep learning models, starting with a simple example based on linear regression. We covered important topics in machine learning such as parameter estimation and backpropagation, loss functions, and diverse neural network layers. We described how to set up a deep learning programming environment that will be used throughout this book. After installing our deep learning programming environment, we trained and evaluated a simple neural network model for the classification of handwritten digits.

In the next chapter, we will cover generative models, explaining the advantages and disadvantages of each class of generative models, including GANs.