Good books or resources for better understanding mathematical notation used in Machine Learning / Deep Learning Papers
For a pragmatic introduction to Deep Learning with many Tensorflow code examples I'd recommend
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition
or
Deep Learning with Python.
The latter is from 2017 and therefore a bit outdated but a new edition is expected to be published later this year (the author has just finished a draft version).
Both books explain, support or define all concepts with code examples. While there is no literal translation from math to code, I do think they provide the required understanding by directly using code as a notation.