This post is part of a series - go here for the index. Welcome back! The previous post gave us a lot of theoretical groundwork on triangles. This time, let's turn it into a working triangle rasterizer. Again, no profiling or optimization this time, but there will be code, and it should get us set…
The purpose of deep learning is to learn a representation of high dimensional and noisy data using a sequence of differentiable functions, i.e., geometric transformations, that can perhaps be used…
The good news about Erlang can be summed up at this: Erlang is the culmination of twenty-five years of correct design decisions in the language and platform. Whenever I've wondered about how something in Erlang works, I have never been disappointed in the answer. I almost always leave with the impression that the designers did the “right thing”. I suppose this is in contrast to Java, which does the pedantic thing, Perl, which does the kludgy thing, Ruby, which has two independent implementations of the wrong thing, and C, which doesn't do anything.