09 – AE, DAE, and VAE with PyTorch; generative adversarial networks (GAN) and code скачать в хорошем качестве

09 – AE, DAE, and VAE with PyTorch; generative adversarial networks (GAN) and code

Course website: http://bit.ly/DLSP21-web Playlist: http://bit.ly/DLSP21-YouTube Speaker: Alfredo Canziani Chapters 00:00 – 1st of April 2021 03:24 – Training an autoencoder (AE) (PyTorch and Notebook) 11:34 – Looking at an AE kernels 15:41 – Denoising autoencoder (recap) 17:33 – Training a denoising autoencoder (DAE) (PyTorch and Notebook) 20:59 – Looking at a DAE kernels 22:57 – Comparison with state of the art inpainting techniques 24:34 – AE as an EBM 26:23 – Training a variational autoencoder (VAE) (PyTorch and Notebook) 36:24 – A VAE as a generative model 37:30 – Interpolation in input and latent space 39:02 – A VAE as an EBM 39:23 – VAE embeddings distribution during training 42:58 – Generative adversarial networks (GANs) vs. DAE 45:43 – Generative adversarial networks (GANs) vs. VAE 47:11 – Training a GAN, the cost network 50:08 – Training a GAN, the generating network 51:34 – A possible cost network's architecture 54:33 – The Italian vs. Swiss analogy for GANs 59:13 – Training a GAN (PyTorch code reading) 1:06:09 – That was it :D