Cleancode

Building dashboards to visualize data or the results of experiments is the bread and butter of data people (read: data scientist, engineers, analysts, etc.). Often, these dashboards are hacked together in record time to meet a presentation deadline. Now imagine this: you built a dashboard for showcasing your latest model to your team. Instead of your go-to tool, Streamlit, you decided to try out FastHTML, a shiny new framework that promises better handling and scalability if your dashboard ever needs to go bigger. Your team lead is so impressed with your model that they want to show it to the whole company. That is your chance to shine! With FastHTML, you don’t have to worry about scaling to a bigger audience. But wait: are you sure your dashboard is really working as expected? How can you be certain nothing fails if the CEO happens to use it? Normally, you would go for automated testing, but after scouring the FastHTML documentation on how to do it, you found nothing. ...

In relatively young disciplines, like deep learning, people tend to leave behind old principles. Sometimes this is a good thing because times have changed and old truths, i.e. over-completeness being a bad thing, have to go. Other times, such old principles stick around for a reason and still people over-eagerly try to throw them out of the window. I am no exception in this regard so let me tell you how I “re-learned” the tried and true design pattern of “Composition over Inheritance”. ...

A while ago, I stumbled upon an article about the language Kotlin and how to use it for Data Science. I found it interesting, as some of Python’s quirks were starting to bother me and I wanted to try something new. A day later, I had completed the Kotlin tutorials using Kotlin Koans in IntelliJ IDEA (which is an excellent way to get started with Kotlin). Hungry to test out my new language skills, I looked around for a project idea. As I am a deep learning engineer, naturally I had a look at what DL frameworks Kotlin had to offer and arrived at DL4J. This is actually a Java framework, but as Kotlin is interoperable with Java, it can be used anyway. I had a look at some examples of how to build a network and found this (Source): ...

Deep learning is a discipline where the correctness of code is hard to assess. Random initialization, huge datasets and limited interpretability of weights mean that finding the exact issue of why your model is not training, is trial-and-error most times. In classical software development, automated unit tests are the bread and butter for determining if your code does what it is supposed to do. It helps the developer to trust their code and be confident when introducing changes. A breaking change would be detected by the unit tests. ...