Deep learning has transformed the way we think of software
and what it can do.
But deep neural networks are fragile and their behaviors are often surprising.
In many settings, we need to provide formal guarantees on the safety, security, correctness, or robustness of neural networks.
This in-progress book covers foundational ideas from formal verification and their application to reasoning about deep learning.
You can get all available chapters
as one pdf or access individual chapters below.
Keep in mind that the book is constantly evolving.