The Melanie Mitchell's book is mostly a high level overview. It's a really good introduction to the field but you will not become a good practitioner by just re

The Melanie Mitchell's book is mostly a high level overview. It's a really good introduction to the field but you will not become a good practitioner by just reading the book. If you want the mathematical details then d2l.ai so far seems pretty good but they use MXNet for the code. fast.ai uses PyTorch and in the later lessons introduces Swift4Tensorflow which bakes differentiable programming into the compiler. PyTorch is more popular so more jobs are likely to have code bases that use PyTorch. So I'd say it depends on your use cases. If you want high level understanding then go with Melanie Mitchell's book. If you want more technical details then d2l.ai or fast.ai are good choices. There's also "Deep Reinforcement Learning Hands-On" by Maxim Lapan. That one is also pretty good but I'm not far enough along to give a review one way or another.