In this first part, you’ll learn the fundamentals to get started building deep learning models. We start with the basic principles and steps of deep neural networks (DNN), with lots of diagrams illustrating these steps along with code snippets to implement the steps. I’ll describe each step and then walk you through the code. Next, we cover the principles and steps of convolutional neural networks (CNN). I’ll walk you through the seminal design patterns behind early state-of-the-art ConvNets, VGGs, and ResNets. You’ll learn how to code each one of these model architectures, with complete code accessible from our public GitHub repository.