Or... Just do the fast.ai course and then find a project that interests you and learn, on your own (with the many resources that are available across the internet), what you need to solve your problem.
I see that you have a capstone entry at the end of your list, but learning with no immediate goal or project to apply it to seems like the quickest way to burn out on what can be a challenging topic for most.
This is a fantastic book that assumes no prerequisites other than knowing python, and takes you through the fundamentals of DL. It has very intuitive and easy to follow explanations, and doesn't use any libraries other than NumPy, so you're building the whole thing yourself, from scratch.
This is kind of the opposite of the previous one, it doesn't go into math and theory, instead it guides you through building several practical projects with a very simple to use DL library(keras). It's a great way to gain practical experience in addition to theory from the previous book. Also has no prerequisites other than python, and makes it very easy to get started.
Extremely brilliant high-level concise overview of how ANNs work. I highly recommend you get started here. You should also check out his videos on calulus and linear algebra, they're fantastic way to learn the math you need.
- Khan Academy videos - one of the easiest ways to learn the math prerequisites.
The leading textbook in Artificial Intelligence. It's not the fastest way to get started, but it's considered one of the best AI textbooks ever written.
I see that you have a capstone entry at the end of your list, but learning with no immediate goal or project to apply it to seems like the quickest way to burn out on what can be a challenging topic for most.
For more on this listen to (https://youtu.be/IPBSB1HLNLo?t=31m2s)