I want to be a good scientist and science is a social endeavour. Crisp communication is essential. Learning to write well, like everything else, is a process. Here are some resources I often go back to.
The Writing Well Handbook by Julian Shapiro
From Wikipedia
Chekhov's gun is a dramatic principle that states that every element in a story must be necessary, and irrelevant elements should be removed.
While, I am not sure I would agree with this principle for drama (for which it was originally emphasized), I think this is the principle for any formal communication, including mathematical and other scientific writing 1.
How to write a great research paper by Simon Peyton Jones
How to read a paper by Srinivasan Keshav
Mathematical Writing by Donald E. Knuth, Tracy Larrabee, and Paul M. Roberts
I Want To Be a Mathematician: A Conversation with Paul Halmos
Writing resources by Iain Murray
Speech is often a medium through which writing is communicated and also essential.
How to give a great research talk by Simon Peyton Jones
How To Speak by Patrick Winston
I was not happy with some of the sentence structures in a paper I wrote recently. I am deliberately noting these rules here for a quick reference whenever I write long-form public texts. My kb will be exempt, to allow faster writing for now. This is a summary from the excellent book by _ William Strunk Jr._, The Elements of Style.
Moses' laws can be rewritten as the law of Moses.
supervised, unsupervised, and reinforcement learning.
Linear regression, one of the most popular methods, is an example of supervised learning.
Linear regression is a regression method, but logistic regression is a classification method v/s While linear regression is a regression method, logistic regression is a classification method.
She is the one who should be replaced by She.
the Bayesians and frequentists should be the Bayesians and the frequentists
Neural networks have hardly advanced our understanding of the human brain, though they are behind many breakthroughs in computer vision. v/s Neural networks are behind many breakthroughs in computer vision, but they have hardly advanced our understanding of the human brain.
Of course, we often beat about the bush to fill those 8 pages. ↩