An interactive book on deep learning. Much easy, so MXNet. Wow. [Straight Dope is growing up] ---> Much of this content has been incorporated into the new Dive into Deep Learning Book available at https://d2l.ai/.
In this chapter, you say the noise on the real function is drawn from a Gaussian with mean 0 and variance 0.1.
However, your code draws the noise from a Gaussian with mean 0 and variance 0.0001, which is a lot less. You multiply a N(0,1) Gaussian by 0.01 (the stddev). To get variance 0.1, you'd have to multiply by sqrt(0.1).
Thanks for the heads up! There have been some code changes need to revise to make code and text agree (of coyrse, maybe it was borked in the first place!)
In this chapter, you say the noise on the real function is drawn from a Gaussian with mean 0 and variance 0.1. However, your code draws the noise from a Gaussian with mean 0 and variance 0.0001, which is a lot less. You multiply a N(0,1) Gaussian by 0.01 (the stddev). To get variance 0.1, you'd have to multiply by sqrt(0.1).