Some initial impressions:
- I really like Andrews teaching style, which is why I took the course. If you are familiar with his machine learning coursera class and enjoy it, you will enjoy this as well. It really feels like a seamless continuation of the ML course and the concepts taught there are helpful. You may want to go through that course first to learn the basics, but if your math is solid you can jump right in to this.
- the course teaches python, numpy, and tensorflow. Some folks had trouble with Octave in the ML course, so many will appreciate the stack being taught here.
- there is lots of foundational mathematics. Some like that (I do) and some don't. If you are not interested in core calculus or linear algebra details and just want to learn applied deep learning through code, you may enjoy the fast.ai courses more (which to me felt a bit cargo culty)
- it's still early in the specialization for me so take the above with a grain of salt!