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 book covers foundational ideas from formal verification and their adaptation to reasoning about neural networks and deep learning.
翻译:深层次的学习改变了我们对软件及其所能做的事情的思考方式。 但深层的神经网络是脆弱的,它们的行为往往令人吃惊。 在许多环境下,我们需要对神经网络的安全、保障、正确性或稳健性提供正式的保障。 这本书涵盖了从正式的核查到基本理念,以及它们适应神经网络和深层学习的理论。