I've seen Distill focus on publishing research with practical applications, so I'd say a mix of both, leaning towards research with a strong emphasis on real-world use cases.

I think a machine learning journal would be most valuable if it focused on publishing practical applications, as that's where many of us struggle to implement research findings effectively.

I've seen Distill, they seem to focus on making complex ML concepts accessible, so I'd guess a mix of both research and practical applications, but leaning towards explaining how models work in real-world scenarios.

I think a machine learning journal would be most valuable if it focused on publishing practical applications, to help bridge the gap between research and industry.

Would it be similar to the Journal of Machine Learning Research, which focuses on publishing research papers, or more like Towards Data Science, which has a broader range of topics?

I've seen Distill focus on making ML research more accessible, so I'd guess it leans towards publishing research with practical implications. What do you think about reproducibility in ML journals?

Would it be similar to the Journal of Machine Learning Research, which focuses on publishing research papers, or more like Towards Data Science, which has a broader range of topics?

I think a machine learning journal would be most valuable if it focused on publishing practical applications, to help bridge the gap between research and industry.

I think a machine learning journal would be most valuable if it focused on publishing practical applications of ML models, as it would help bridge the gap between research and real-world implementation.