I think the ML results and demo posts are mostly getting upvotes because the results are fascinating and the posts are often excellently written. Image manipulation in particular lends itself towards posts that are appealing both to people casually skimming articles and to people looking for some technical depth.
However, I'd appreciate more comments of course ;)
I do research in ML so I love seeing these posts, but the fact that words like neural networks has become such a buzzword is somewhat disappointing. I remember all the buzz when Swiftkey released their "neural network" keyboard simply because of the name.
I think it has to do with the fact that HN is a very diverse mix of folks (technical, non-technical, working at startup, working at big co, web devs, mobile devs, infra dev, etc). There isn't a concentration of ML-technical folks like you might find on /r/machinelearning (decent technical ML discussion). Instead you have here a mix of futurology speculation and tutorial discussion - lots of introductory material of highly varying quality (since the people upvoting don't really know if it's good), science fiction passed off as credible opinion, highly technical ML posts that get upvoted a lot but no comments, etc.
Nobody thinks SVM is cool anymore :S
Part of this is due to, I think the immense amount of marketing pushed by Big Corp on their recent AI successes.
Regarding the post: This seems like a useful resource. When I read many of these papers, a code supplement makes understanding it so much better. I do a lot of research with RNNs with respect to language modeling and implementing various models when I started researching this field was very useful to get a better understanding. I got great feedback on my implementations where people said it helped them understand.