This is a quick cheatsheet for stylizing Jekyll blog posts. Jekyll uses Markdown for formatting, so all the Markdown commands work in Jekyll. There are also some additional things one can do with Jekyll.

In Gathering text from Project Gutenberg we retrieved text from Project Gutenberg and built a couple of functions along the way to help. The functions make us more efficient, but what we really need is a class for this work. This will allow us to store and analyze many different texts very efficiently. Let’s build that.

Let’s take the previous analysis of a single text and formalize it so we can use it against many texts. From there we’ll explore light verb use across many texts.

Light verbs are verbs that have little meaning on their own, almost like filler verbs. While using light verbs in writing is certainly not wrong, writers need to be careful about using them too often. They can fill writing with fluff. Sentences with light verbs often contain nouns that could replace the light verb.

This notebook takes off from Visualize Parts of Speech 1, which ended with a visualization from a single text. In this notebook, we look at how to visually compare the part of speech usage in many texts.

Do good writers use fewer adverbs than poor writers? Is it possible to improve ones writing by looking at the relative distribution of different parts of speech (POS)? I was curious about this and wanted to investigate. The first step towards determining this is to find out what a “normal” POS distribution is. To do that, we’ll explore parts of speech usage in Great Expectations by Charles Dickens.

CUDA used to be an acronym for Compute Unified Device Architecture, but now it’s no longer an acronym. It’s just CUDA. CUDA is basically C for GPUs. Just like operations in NumPy use C and go much faster, the same is true for CUDA operations in GPUs.