Good books or resources for better understanding mathematical notation used in Machine Learning / Deep Learning Papers

Can anybody recommend any books or resources that would be good for better understanding the mathematical notation that is often seen in ML/DL papers? Preferably with examples of how the notation can be transcribed into tensorflow code. I did Linear Algebra and Vector calculus in uni a long time ago but it is a long way back and I would like to get back up to speed with it.

This question I think is not what I am looking for because I would like to see an emphasis on how one can transcribe the math into code.

Topic books

Category Data Science

1
answered at

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The latter is from 2017 and therefore a bit outdated but a new edition is expected to be published later this year (the author has just finished a draft version).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.

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