- Understanding deep learning by Prince[1]. Has both theoretical and programming exercises.
- Deep learning - Foundations and concepts by Bishop[2].
- Deep learning architectures - mathematical approach - Calin[3].
- Mathematical theory of deep learning by Petersen et al[4]. Only handful of exercises each chapter.
- Dive into deep learning[5]. This one is more practical programming hands-on. The exercises is a mix between programming exercises and theoretical.
- EECS 182/282A | Deep Neural Networks 2025 (not a book but a course by Berkley)[6]. I only skimmed the assignments, but they seem very interesting. A mix of theoretical and practical.
I think Bishop is probably the best one for theoretical exercises, balancing between not too difficult but not too easy either. However, if you also want programming exercises, Prince has lots of those. Berkley also seems very interesting, modern and encompassing.
[1] https://udlbook.github.io/udlbook/
[2] https://www.bishopbook.com/
[3] https://link.springer.com/book/10.1007/978-3-030-36721-3