recent advances in deep learning multi-domain wavenet autoencoderwith shared encoder and domain-independent latent space that is trained end-to-end on waveforms paper live face de-identification in video this method enables face de-identification in fully automatic setting for live videos at high frame rates using feed-forward encoder-decoder network architectureconditioned on the high-level representation of person' facial image paperc oncluding thoughts we would like you to thank youthe readerfor the time and interest you've taken to study the subject of deep learning by reading this book we sincerely appreciate your efforts invested in this book and hope that we have been able to deliver up to your expectations the subject of deep learning is so vast and dynamic that one would need to conduct continued research to keep up with the pace of the innovations our focus with this book has been to deliver healthy combination of abstract yet intuitive information on the subject (with minimal math operationsapologies if the equations were overwhelming)while blending the much-needed practical implementations with real-life datasets using the leading tool in industry and academia (pytorchwe would appreciate your thoughts and feedback