Summary

In this chapter, we have summarized key concepts required to get started with the real-world implementation of deep learning systems. We described core concepts from linear algebra that are central to understanding the foundations of deep learning technology. We provide a hardware guide to deep learning by covering various aspects of GPU-based implementation and what is a right hardware choice for application developers. We outline a list of most popular deep learning software frameworks that exist today and provide a feature-level parity as well as a performance benchmark for them. Finally, we demonstrate how to set up a cloud-based deep learning application on AWS.

In the next chapter, we will introduce neural networks and outline a self-start module to understanding them in greater details.