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tokens: from low SNR, individual exposures. At high SNR, we obtain abundances precise to ≈0.01 to and even at low SNR (SNR ≈ 50) we get precisions of ≈0.02 to for most elements. These precisions are confirmed by looking at the scatter in the abundances within open clusters, which we find to be . We also recovered the expected abundance trends in globular clusters, but found that the censoring in the network is crucial to obtain this, because training on the full spectrum for all elements causes the NN to depend too strongly on correlations between elements within the training set and therefore to fail when these correlations are absent, like in globular clusters. We also demonstrated that a large NN can work well with a limited amount of training data, finding barely degraded performance for training sets that only consists of thousands of spectra. The speed and flexibility of NN s mean that they are a highly useful tool for spectroscopic data analysis. They allow the results obtained from a detailed analysis of a small calibration set to be transferred to an entire large data set of millions of spectra in a matter of minutes and can thus be a great aid in the development of the next generation of spectroscopy tools. They could also be of use in earlier stages of the data processing, for example, for homogenizing spectra taken with different instruments (e.g. the northern and southern APOGEE spectrographs) or for removing instrumental systematics such as persistence (e.g. Jahandar et al. 2017 ). Such applications would be easy to pursue with the astroNN software package described in the Appendix. APPENDIX: ASTRONNN: A PYTHON LIBRARY FOR DEEP LEARNING IN ASTRONOMY Data-driven tools are becoming more and more popular in astronomy and deep learning in particular is becoming a popular technique to deal with big data sets. When we started on the research for this paper, we realized that a python package that (i) has a focus on deep learning, (ii) contains tools and data sets that are relevant to astronomers, (iii) is easy to use and well tested against errors, and (iv) acts as a platform to share astronomy-oriented NN s is needed to advance research in this area. astroNN is a python package for deep learning in astronomy designed to fulfill the points stated above. astroNN relies heavily on Tensorflow (Abadi et al. 2016 ), a standard deep learning python library, and is easy to set up on all common platforms. astroNN employs custom methods for storing and sharing models: models are saved in a folder and consist of the NN itself in HDF5 format, the training history in CSV format, model parameters, and the names of output neurons as well as normalization parameters in JSON format. Users simply have to give the reduced data as numpy array(s) to astroNN and get outputs back as numpy array(s); normalization of the inputs and outputs is handled internally. At this point, astroNN as used in this paper consists of about 8300 lines of code in the modules, 1200