I don't know if it is math but formal scientific writing can make even simple concepts hard to grasp for me, a developer.
During my studies, I had to assist to a conference by a mathematician explaining some image processing algorithm. It took me half an hour to realize that all that was a good old "make each pixel the weighted average of its surroundings" blur, also known as a convolution filter. And I spent the other half hour completely lost trying to understand what was special about his blur. I think no one actually understood anything, in fact, I probably did better than most because I was familiar with convolution filters from personal experience.
Had he explained it in terms of pixels and loops instead of what was probably (for us) a Greek letter soup, I am sure we would have understood.
There is a chronic problem in audio/image processing where domain experts have developed their own notation and terminology and do a really poor job connecting it to the simple concepts they represent.
> Had he explained it in terms of pixels and loops instead of what was probably (for us) a Greek letter soup, I am sure we would have understood.
Unfortunately concretizing the math to realizations leaves you with a cookbook for implementing the least interesting bits of signal processing. Looking at some matrix multiplication as for loops doesn't teach you how a filter works.
Like just to give an example, I don't think it's possible to come up with an explanation of the Remez Exchange Algorithm in any physically meaningful context since it's fundamentally about recognizing the mathematical properties of a higher order abstraction of the system designed with it. But we can be a bit smarter about how we write, since "an iterative algorithm used to find simple approximations to functions, specifically, approximations by functions in a Chebyshev space that are the best in the uniform norm Lā sense" is Wikipedia word soup that belies the concepts and connections that make it useful. It's just hard to do it in a sentence and not a semester.
During my studies, I had to assist to a conference by a mathematician explaining some image processing algorithm. It took me half an hour to realize that all that was a good old "make each pixel the weighted average of its surroundings" blur, also known as a convolution filter. And I spent the other half hour completely lost trying to understand what was special about his blur. I think no one actually understood anything, in fact, I probably did better than most because I was familiar with convolution filters from personal experience.
Had he explained it in terms of pixels and loops instead of what was probably (for us) a Greek letter soup, I am sure we would have understood.