Depends on what you want from a (text)book. In my mind books should be authoritative, they should include things that have had some thought put into them and are fairly well studied/verified. Modern advances in deep learning/ML are exciting but are very often not this. I would not a read a book which is just some recent hype papers from NeurIPS/ICML stapled together.
It depends on the particular subtopics it covers. Machine Learning: A Probabilistic Perspective is from 2012 and it's still a great resource, although Murphy's newer book will certainly cover more up-to-date material.
From a quick glance, it looks like it covers much of the same material as this text [1]. I wonder how they compare.
[1]: https://www.cambridge.org/core/books/understanding-machine-l...