By Ilia Karmanov, Data Scientist at Microsoft This post is for those of you with a statistics/econometrics background but not necessarily a machine-learning one and for those of you who want some guidance in building a neural-network from scratch in R to better understand how everything fits (and how it doesn't). Andrej Karpathy [wrote](https://medium.com/@karpathy/yes-you-should-understand-backprop-e2f06eab496b) that when CS231n (Deep Learning at Stanford) was offered: >"we intentionally designed the programming assignments to include explicit calculations involved in backpropagation on the lowest level. The students had to implement the forward and the backward pass of each layer in raw numpy. Inevitably, some students...