The book Mastering OpenCV helped me a bunch several years ago during implementation. The best advice I can give along with this book is to try and understand the fundamental geometric problem that computer vision tries to solve. It's been the same for like 30-50 years...we've just thrown some ML at it for these past 10-20. For that, I recommend a copy of Trucco and Veri's book that you can probably find floating around online (link below). Good luck!

https://github.com/MasteringOpenCV/code

https://www.amazon.com/Introductory-Techniques-3-D-Computer-...

This is pretty old school, but I recommend Multiple View Geometry by Hartley and Zisserman (http://www.robots.ox.ac.uk/~vgg/hzbook/) to get through the fundamentals...it's really good to understand the geometric foundations for the past 4 decades. Along the same lines, you have Introductory Techniques for 3-D Computer Vision by Trucco and Verri (https://www.amazon.com/Introductory-Techniques-3-D-Computer-...), which also goes over the geometry and the fundamental problems that computer vision algorithms try to solve. It often does come down to just applying simple geometry; getting good enough data to run that model is challenging.

If you just throw everything into a neural network, then you won't really understand the breadth of the problems you're solving, and you'll be therefore ignorant of the limitations of your hammer. While NNs are incredibly useful, I think a deep understanding of the core problems is essential to know how to use NNs effectively in a particular domain.

After getting a grip on those concepts, Szeliski's Computer Vision: Algorithms and Applications (http://szeliski.org/Book/) had some really amazing coverage of CV in practice. Mastering OpenCV (https://www.amazon.com/Mastering-OpenCV-Daniel-Lelis-Baggio/...) was very useful when actually implementing some algorithms.

This (http://www.amazon.com/Introductory-Techniques-3-D-Computer-V...) is the best book on Computer Vision I 've come across. I first encountered it when trawling Stanford courses for good problem sets and research projects. Sebastian Thrun used to teach a class with this book (http://robots.stanford.edu/cs223b04/). It is fairly expensive and fairly mathematical but it is worth every cent you pay for it (imho).

Good Luck !