Where can I *actually learn* programming? (as DH and otherwise)

To my surprise, lots of people regularly ask me where I learned to program. I have lots of posts on the subject and even multiple categories on the blog concerning the topic but maybe they’re “too disguised” under obscure titles for willing learners to actually find them. So I decided to give you a short summary with the key takeaways of what I’ve written on the topic so far and the most important links – boths to my other detailled blogposts and also on the resoures I would recommend as of now. However, before the “short summary” of takeaways and suggestions for willing learners, let me start with a deep dive into the very personal side of the question “Where did you actually learn to program?”. Now where did I actually learn to code? To answer the question “Where did I actually learn to program?”, we need to talk about my journey first. And.. Well, in order to answer this

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Machine Learning for the Humanities: A very short introduction and a not-so-short reflection

Machine Learning is one of those hot topics at the moment. It’s even starting to become a really hot topic in the Humanities and, of course, also in the DH. But Humanities and Machine Learning are not the most obvious combination for many reasons. Tutorials on how to run machine learning algorithms on your data are starting to pop up in large quantities, even for the DH. But I find it problematic that they often just use those methods, just show you those few lines of code to type in and that’s it. Frameworks have made sure that ML algorithms are easy to use. They actually have a super-low entry level programming-wise thanks to all those libraries. But the actual thing about ML is that you need to understand it or it’s good for nothing. (Ok, I admit there are some uses which are pretty straightforward and don’t need to be fully understood by users, such as Deep Learning powered

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News on the DH and Gender Equality

A few weeks ago, I posted The Computational Humanities and Toxic Masculinity? A (long) reflection   and didn’t know what reaction to expect. It would either be a shitstorm or get ignored, I assumed. It turns out, however, that the timing might have been just right, since I was contacted by the (informal) Turing Institute reading group – they wanted to discuss my blog post. I was honoured that they did and that I could participate. Here I wanted to share some new insights and reflection prompts this discussion has sparked for me. First things first: Thanks for everyone who participated! First of all, I was happy this discussion happened at all. That’s exactly the type of thing I had aimed for with the post, yet I was pretty surprised it actually happened. After all, you normally don’t always get you want. Maybe it’s that help will always be given at Hogwarts to those who ask for it. 😉 So anyway,

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The Computational Humanities and Toxic Masculinity? A (long) reflection

Today, I want to talk about the Computational Humanities discussion from last year and finally say something with regard to the gender issues in it. I called this post ‘Toxic Masculinity?’ and maybe that’s provocative to you. But maybe it also correctly describes a situation which could come to pass in the DH – a discipline which considers itself so forward-thinking – if we don’t take lurking issues seriously. So this is my reaction. Some disclaimers and considerations I wanted to respond to this discussion when it originally came up. Then I didn’t dare  to post it because I was kind of afraid there would be a bad reaction, especially as the slightly-shitstorm-like situation was still fresh and I didn’t want to offend anyone. So I didn’t and now it’s all kind of too late and not up-to-date anymore. But since the general subject is still relevant to me, I decided to use this Corona situation to write up this

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