As a person who did a lot of Deep Learning... learning in 2017, I think this was my favorite.
Fast.ai was fine, but I felt like most of my learning for the things I cared about came from reading research papers, watching Karpathy's CS231n lectures, and blog posts that went into detail on particular concepts.
But when at certain points I felt confused on certain concepts, Geron's book did a pretty good job explaining things slowly and in great detail, especially with respects to the code he wrote. It's still a book I'll pick up for 20-40 minutes every other day to help my mind recall about how something works.
Funnily enough, I've spent the last few months reading Sutton/Barto's Intro to Reinforcement Learning (along with Silver's lectures on DeepMind's YouTube Channel) and only realized Geron touches upon RL a little bit in the latter part of the ML book.
Fast.ai was fine, but I felt like most of my learning for the things I cared about came from reading research papers, watching Karpathy's CS231n lectures, and blog posts that went into detail on particular concepts.
But when at certain points I felt confused on certain concepts, Geron's book did a pretty good job explaining things slowly and in great detail, especially with respects to the code he wrote. It's still a book I'll pick up for 20-40 minutes every other day to help my mind recall about how something works.
Funnily enough, I've spent the last few months reading Sutton/Barto's Intro to Reinforcement Learning (along with Silver's lectures on DeepMind's YouTube Channel) and only realized Geron touches upon RL a little bit in the latter part of the ML book.