Deep Learning Course (NYU, Spring 2020) - Week 8 – Practicum: Variational autoencoders

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42.5 hours of video
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Access on mobile and TV

Summary

Keywords

Deep Learning Yann LeCun autoencoder over-complete generative variational autoencoder posterior prior KL divergence relative entropy PyTorch

Full Transcript

Course website: http://bit.ly/DLSP20-web Playlist: http://bit.ly/pDL-YouTube Speaker: Alfredo Canziani Week 8: http://bit.ly/DLSP20-08 0:00:00 – Week 8 – Practicum PRACTICUM: http://bit.ly/DLSP20-08-3 In this section, we discussed a specific type of generative model called Variational Autoencoders and compared their functionalities and advantages over Classic Autoencoders. We explored the objective function of VAE in detail, understanding how it enforced some structure in the latent space. Finally, we implemented and trained a VAE on the MNIST dataset and used it to generate new samples. 0:02:35 – Autoencoders (AEs) vs. variational autoencoders (VAEs) 0:16:37 – Understanding the VAE objective function 0:31:33 – Notebook example for variational autoencoder

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