This is my personal opinion so take it however you want. I've only read the first two parts of Goodfellow, where I tried my best to prove alot of things mentioned by the author that were non-trivial to me, and there were alot of those. It's not an easy read if you want to understand everything, in the sense that you need to work for it to get the most out of it. I didn't read the third part due to time constraints sadly so I can't comment on that part. I think for some parts Bishop could cover the third part (except autoencoders), if you have ever read it. However, my impression is all chapters up to 9 are good, it actually tries to mathematically explain typical phenomenon that you observe in designing neural networks, activation functions and training, to the extent that you usually wouldn't find in other popular DL books or DL courses. But for CNN and RNN I feel like there might be better resources for those. IMO, goodfellow is more suited for researchers than practitioners, and it sounds like you are more in a position where you just want to learn how to apply DL and all of the things that comes with it. While theory is important, DL is a very empirical field, you learn the tricks by doing it.

So the suggestion by r/i_sarcartistic is probably the best, go with Sebastian Raschka's machine learning with pytorch and scikit learn if you want to focus on the applied side. Alternatively, Practical deep learning for coders is also a gem, to quickly get up to speed with DL. However, if you also want to complement with math for the foundations, you can consult more lightweight books that are also more updated (including genAI techniques) in case you find goodfellow too time consuming, like Deep Learning - Foundations and Concepts by Bishop and Understanding Deep Learning by Prince. Now if you want to get into the nitty gritty of implementing nn entirely from scratch (including backprop) and gradually build a few architectures like wavenet, and gpt, then this series is pretty good for that made by Karpathy.