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We introduce QuasarNET, a deep convolutional neural network that performsclassification and redshift estimation of astrophysical spectra withhuman-expert accuracy. We pose these two tasks as a \emph{feature detection}problem: presence or absence of spectral features determines the class, andtheir wavelength determines the redshift, very much like human-experts proceed.When ran on BOSS data to identify quasars through their emission lines,QuasarNET defines a sample $99.51\pm0.03$\% pure and $99.52\pm0.03$\% complete,well above the requirements of many analyses using these data. QuasarNETsignificantly reduces the problem of line-confusion that induces catastrophicredshift failures to below 0.2\%. We also extend QuasarNET to classify spectrawith broad absorption line (BAL) features, achieving an accuracy of$98.0\pm0.4$\% for recognizing BAL and $97.0\pm0.2$\% for rejecting non-BALquasars. QuasarNET is trained on data of low signal-to-noise and mediumresolution, typical of current and future astrophysical surveys, and could beeasily applied to classify spectra from current and upcoming surveys such aseBOSS, DESI and 4MOST.

QuasarNET: Human-level spectral classification and redshifting with Deep Neural Networks