I wrote about some of the things that go into creating a really great portfolio project for machine learning. For this post I'm less interested in the technical achievements than I am in how it is presented.
I setup a new Paperspace project that uses a custom Docker image to provision its environment, saving me a bunch of initial installation time and dependency bug pain. A huge productivity win!
I'm reading Elton Stoneman's 'Learn Docker in a Month of Lunches' and blogging as I learn along the way. In chapters 1-3 we learn about the context for Docker as well as some basic commands for running and building containers.
I used the under-appreciated tool FiftyOne to analyse the ways that my object detection model is underperforming. For computer vision problems, it's really useful to have visual debugging aids and FiftyOne is a well-documented and solid tool to help with that.
The parse, yarl and datefinder packages are all ways in Python to help parse input data of different formats and types. Nothing essential here, but useful nonetheless.
I iterated through several prototypes to get to a script that could autogenerate synthetic training data for my computer vision model. I hoped to bootstrap my training to get a bit jump in model performance.
Chapter 10 covers the last of the user-defined types explored in 'Robust Python': classes. We learn what an 'invariant' is and how to decide whether to use a data class or a class when rolling your own types.
Chapter 9 of 'Robust Python' dives into the uses of data classes, a user-defined datatype in which you can store heterogenous data together. They help formalise implicit concepts within your code and as a result also improve code readability.
The eight chapter of Patrick Viafore's book, 'Robust Python', gets into enums which you can use when you have a grouping of some constants that belong together.
Reflections on the sixth and seventh chapters of Patrick Viafore's book, 'Robust Python'. We slowly wind down our discussion of type hints in Python code and think through using `mypy` and how to introduce type hints to a legacy codebase.
Reflections on the fifth chapter of Patrick Viafore's book, 'Robust Python'. We learn about how to use type annotations when collections (lists, dictionaries and sets, primarily) are involved.
Reflections on the fourth chapter of Patrick Viafore's recent book, 'Robust Python'. We learn about the different options for combining types and constraining exactly which sets of types are permitted for a particular function or variable signature.
Some early thoughts on the benefits and possible drawbacks of using fastai's 'nbdev' literate programming tool which is a suite of tools that allows you to Python software packages from Jupyter notebooks.
