In the previous article of this series, we discussed the Dreyfus model, an abstraction that lets you understand the progression we humans go through when learning a new skill.
In this article, we will discuss another useful model. In this case, the subject is our own amazing brain. Have you
I recently started reading Pragmatic Thinking and Learning: Refactor Your Wetware by Andy Hunt (Yes, the guy from The Pragmatic Programmer). It's a cool book, so cool in fact that I believe it's one of the best things I've read on the topic.
I wanted to write a series summarizing
In Rails, there are two main ways of creating many-to-many relationships between models:
This is Quick tips, so let's cut to the chase.
This is the easiest way to establish a
In a previous article, we learned about supervised learning: using labeled examples for creating models that solve a variety of tasks such as classification and regression.
There is another type of learning that doesn't require labeled examples: unsupervised learning. As it turns out, unlabeled data is much more common than
Categorical data is extremely common in most real-world machine learning applications. The main problem is that most algorithms don't really know how to manage categorical data: they are really good at working with numbers but don't really understand the concept of category. Because of this, it's important to have a