We cover rational functions here. In particular, the book defines the Riemannian sphere (C + infinity) and is the 1-point compact superset of C. This helps us define compact sets for which we can approximate functions using rational functions. This was also a pretty paltry, but interesting, chapter; the next chapter covers for more abstract functions.
This chapter covers more interesting properties of the maximum modulus principles. In particular, there are many interesting exercises for applying this principle to unbounded regions for functions which grow at a low rate (order of magnitude). This makes it somewhat interesting, but less abstract.
This chapter covers the basics of harmonic analysis and introduces information like the Cauchy-Riemann equations. It serves as a teaser chapter and, as such, most of the questions are rather bashy calculations. As an overall examination, I will say that the bash in real analysis is much less then the bash in complex, a worrisome sign since I hate bashing.
I recently happened upon quite an interesting paper, titled “GAN Q Learning”. I’ve spent some time playing around with the algorithm, and, during that time, I’ve gained valuable insight into the inner workings of both Deep Learning Paradigms. But, before I can give a full explanation, I’ll give a background introduction to both.