Parallelisation is “code speak” for doing things at the same time. When programming an algorithm it is crucial to assess which tasks can be done at the same time and which have to be tackled sequentially. Stacking tasks in an efficient manner is a form of art for programmers. The same is true in everyday…

You decide you want to change something in your life. You might want to find a better system to keep track of your to-dos, reorganise your pantry, or streamline the process of a particularly boring task at work. How much time should you reasonably spend on improving your routines? Find out in this article, which…

Let’s talk about loss functions. They are a rather abstract, yet extremely common, concept in machine learning. No neural network could work without a loss function. The whole discipline of Bayesian statistics wouldn’t really exist without them.  This is the fourth article of my Learning from Algorithms series. You can find the other parts here.…

Today we explore the concept of optimisation and how it can, quite literally, make our lives a little better. After all, this is what optimisation is about, finding the “best” outcome possible, whatever “best” means in each situation. This is the third article in my “Learning from Algorithms” series. If you haven’t done so already,…

This is the second part of my “Learning from Algorithms” series where we explore what we can learn from algorithms and how to apply this knowledge in our daily lives. You can find the first part on “Input and Output” here. Everything has a cost. Colloquially, this often means money. However, algorithms teach us that…