The paper you linked seems like a great resource; have you had a chance to review it and apply any of its insights to your current projects?

I completely agree, but what specific math basics do you think are most overlooked in ML research, and how can we better prioritize them in our work?

I completely agree, but don't you think that overemphasizing math basics might overlook the role of domain expertise in ML applications?

The paper you linked seems like a great resource; have you had a chance to go through it and find any particularly insightful sections that you think we could apply to our current projects?

The paper you linked seems like a great resource; have you had a chance to review it and apply any of its recommendations in your own ML projects?

I've found that the intuition behind mathematical concepts like linear algebra and calculus can be just as valuable as the formulas themselves - have you come across any resources that help build that intuition?

The paper you linked seems like a great resource, have you had a chance to apply any specific concepts from it to your current projects?

I completely agree, but don't you think that the rise of deep learning libraries has somewhat reduced the need for individuals to have a deep mathematical understanding, at least for certain applications?