This is part of a series on ML for generalists, you can find the start here.

We need images to train our model, so let's create some synthetic data. We'll need images rotated at varying degrees and we'll also need our ground truth, the correct answer: how many degrees we rotated the image by.

Without the ground truth, we can't tell our model how wrong its predictions are so we can't train it and improve its answers.

You can let your eyes glaze over for this code, it's not important, it just gets us a dataset we can work with:

• Creates an output directory
• Generates the requested number of images:
  • each image is a 480x480 square
  • contains random lines of text which may be in different sizes
  • is rotated by a random number of degrees
• Writes an answersheet.json with the ground truth (correct answer) rotation for each image

This is part of a series on ML for generalists, you can find the start here.

I'm a fan of uv from Astral for managing Python projects. It takes care of package management, Python versions and virtual environments. Use whatever you prefer, but the commands below assume uv.

I'm a generalist software engineer. I think I'm a generalist in most aspects of my life. I've never wanted to be pigeonholed as a front-end or a backend dev. Same for languages: I'm not a Python person or a Kotlin kid. I can't solve complete problems without knowing a little about a lot.

I always considered machine learning an exception. A domain too specialised to understand, with lots of maths and academic knowledge required. The closest I came was translating Jupyter Notebooks into applications that could run in production.

That's changed. Even when the next AI winter comes (and it'll be a cold one), people will still expect that little sprinkle of ML magic in their applications.

I've written the coding standards at half a dozen companies by now. I generally don't care about where you put the curly braces or if else's are cuddled, I'm happy to let an opinionated formatting tools decide that for me.

For every rule, I should be able to explain why it's valid. If my explanation is weak, so is my rule.