How does Keras 1d convolution layer work with word embeddings – text classification problem? (Filters, kernel size, and all hyperparameter)

I am currently developing a text classification tool using Keras. It works (it works fine and I got up to 98.7 validation accuracy) but I can’t wrap my head around about how exactly 1D-convolution layer works with text data.

What is the problem with reduce()?

There seems to be a lot of heated discussion on the net about the changes to the reduce() function in python 3.0 and how it should be removed. I am having a little difficulty understanding why this is the case; I find it quite reasonable to use it in a variety of cases. If the contempt was simply subjective, I cannot imagine that such a large number of people would care about it.