The high-energy physics (HEP) community has a long tradition of using neural networks and machine-learning methods (random forests, boosted decision trees, multi-layer perceptrons) to solve specific tasks. In particular, they are used to improve the efficiency with which interesting particle-collision events can be selected from the background. In the recent years, several studies have demonstrated the benefits of using deep learning (DL) to solve typical tasks related to data taking and analysis. Building on these examples, many HEP experiments are now working to integrate deep learning into their workflows for many different applications (examples include data-quality assurance, simulation, data analysis, and real-time selection of interesting collision events). For example, generative models, from GANs to variational auto-encoders, are being tested as fast alternatives to simulation based on Monte Carlo methods. Anomaly-detection algorithms are being explored to improve data-quality monitoring, to design searches for rare new-physics processes, and to analyse and prevent faults in complex systems (such as those use for controlling accelerators and detectors).

 The training of models such as these has been made tractable with the improvement of optimisation methods and the availability of dedicated hardware that is well adapted to tackling the highly parallelisable task of training neural networks.  Storage and HPC technologies are often required by these kinds of projects, together with the availability of HPC multi-architecture frameworks (from large multi-core systems to hardware accelerators like Google TPUs).