I've long had an interest in programming languages, with a particular fondness for those falling under the "functional" umbrella. More recently, I've also developed an enthusiasm for machine learning. In the case of the latter, it seems that barely a week goes by without a new attention-grabbing development capturing the headlines. While Python has established itself as the lingua franca within the ML community, it is not without flaws. The "Two-Language Problem," as it has become widely known, has been particularly troublesome. The problem refers to the fact that while Python is easy to understand and fast to prototype, it is notoriously slow to execute. This means that many of Python's most famous libraries are first written in performant low-level languages like C++, which are then wrapped in a high-level API implemented in Python.

While Python will likely continue to dominate machine learning for the foreseeable future, a particularly promising development came with the introduction of Julia. By combining a clean, high-level syntax with low-level performance, Julia stands poised to revolutionize the field of scientific computing. Moreover, it gives me an excellent opportunity to simultaneously pursue two of my passions. I created the website Machine Learning with Julia both to document my own experiences and to provide a collection of practical tutorials for other researchers. I intend to contribute to this project on an ongoing basis as I explore Julia's potential within the rich and exciting world of machine learning.