Coding Tips¶
In this book, we use Python as our programming language. In the main chapters, we will focus on the theories and actual code and skip the basic concepts. To make sure we are on the same page, we shove all the tech stack related topics into this chapter for future reference. It is not necessary to read this chapter before reading the main chapters. However, we recommend the readers go through this chapter at some point to make sure they are not missing some basic engineering concepts.
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This chapter is not aiming to be a comprehensive note on these technologies but a few key components that may be missing in many research-oriented tech stacks. We assume the readers have worked with the essential technologies in a Python-based deep learning project.
Good References for Coding in Research¶
Some skills only take a while to learn but people benefit from them for their whole life. Managing code falls exactly into this bucket, for programmers.
The Good Research Code Handbook is a very good and concise guide to building good coding habits. This should be a good first read.
The Alan Turing Institute also has a Research Software Engineering with Python course. This is a comprehensive generic course for boosting Python coding skills in research.
A Checklist of Tech Stack
We provide a concise list of tools for coding. Most of them are probably already integrated into most people's workflows. Hence we provide no descriptions but only the list itself.
In the following diagrams, we highlight the recommended tools using orange color. Clicking on them takes us to the corresponding website.
The first set of checklists is to help us set up a good coding environment.
The second set of checklists is to boost our code quality.
Finally, we also mention the primary python packages used here.