Hopefully, this chapter served to refresh your memory on deep neural network architectures and optimization algorithms. Because this is a quick reference we didn't go into much detail and I'd encourage the reader to dig deeper into any material here that might be new or unfamiliar.
We talked about the basics of Keras and TensorFlow and why we chose those frameworks for this book. We also talked about the installation and configuration of CUDA, cuDNN, Keras, and TensorFlow.
Lastly, we covered the Hold-Out validation methodology we will use throughout the remainder of the book and why we prefer it to K-Fold CV for most deep neural network applications.
We will be referring back to this chapter quite a bit as we revisit these topics in the chapters to come. In the next chapter, we will start using Keras to solve regression problems, as a first step into building deep neural networks.
