[1] https://www.coursera.org/learn/machine-learning?authMode=log...
Without having read it, I'd recommend the "preliminary maths" part of Bengio et al's Deep Learning book - it teaches both the letters and the language, so to speak, and if the language isn't for you, you'd better throw away the papers and completely concentrate on reading and understanding the implementations that are our there, using the implementation first and foremost and the paper only as a backup to provide explanations when the implementation does mysterious or unexplained things.
You can do deep learning productively without having a PhD, but you won't be able (nor obliged) to read and understand PhD-level academic papers unless you have a solid (i.e., maths or physics or math-rich CS BSc) maths background.
Honestly it's weird getting this far without knowing the symbols well, I went to a significantly poorer and less well staffed school that most of my fellow students, and you're kind of between a rock and a hard place sometimes, because no-one sees it as their job to catch you up, and the people who do know it learned it long ago manually and don't exactly have an easy time teaching you if you ask.
I did the course earlier this year, and I can confirm that you don't need much more than high-school level maths knowledge. If you understand concepts like functions and summation then you're most of the way there, and if you've got some calculus then it should be easy. I came out of it with better mathematical comprehension than when I went in.
I found that I spent a lot of time on the course converting mathematical notation into code (octave/matlab), which is a great way to validate your understanding of the maths. If you're understanding is wrong then it either throws an error, or runs slowly because you failed to map summations etc onto the appropriate parallelised operations.
ML has moved on a bit since the course was designed, but it's still a good way to get familiar with the basics.