Chapter 3. The Computational Side of Deep Learning

This chapter explores how computations are performed on the hardware and how acceleration is achieved through hardware advancements. As discussed in Chapter 1, the power of having a clear understanding of what is happening across the stack of your application, spanning the algorithm, software, hardware, and data, is profound. Limitations and trade-offs could surface from anywhere in your stack, and such an understanding empowers you to make careful, optimal decisions and find the right balance while working within your limitations, especially when scaling.

In Chapter 2, you learned about the foundational concepts of deep learning and worked through the software implementations of a couple of basic problems. In this chapter, you will dive into the details of how that software interacts with hardware. We’ll cover the fundamentals of computation units and specialized hardware for accelerated ...

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