In this first chapter, we will introduce the three deep learning artificial neural networks that we will be using throughout the book. These deep learning models are MLPs, CNNs, and RNNs, which are the building blocks to the advanced deep learning topics covered in this book, such as Autoencoders and GANs.
Together, we'll implement these deep learning models using the Keras library in this chapter. We'll start by looking at why Keras is an excellent choice as a tool for us. Next, we'll dig into the installation and implementation details within the three deep learning models.
This chapter will:
Establish why the Keras library is a great choice to use for advanced deep learning
Introduce MLPs, CNNs, and RNNs – the core building blocks of most advanced deep learning models, which we'll be using throughout this book
Provide examples of how to implement MLPs, CNNs, and RNNs using Keras and TensorFlow
Along the way, start to introduce important deep learning concepts, including optimization, regularization, and loss function
By the end of this chapter, we'll have the fundamental deep learning models implemented using Keras. In the next chapter, we'll get into the advanced deep learning topics that build on these foundations, such as Deep Networks, Autoencoders, and GANs.