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Deep Learning (DL) techniques are now widespread and being integrated intomany important systems. Their classification and recognition abilities ensuretheir relevance for multiple application domains. As machine-learning thatrelies on training instead of algorithm programming, they offer a high degreeof productivity. But they can be vulnerable to attacks and the verification oftheir correctness is only just emerging as a scientific and engineeringpossibility. This paper is a major update of a previously-published survey,attempting to cover all recent publications in this area. It also covers aneven more recent trend, namely the design of domain-specific languages forproducing and training neural nets.

Formal methods and software engineering for DL. Security, safety and productivity for DL systems development