NYU Deep Learning SP21 - 07L – PCA, AE, K-means, Gaussian mixture model, sparse coding, and intuitive VAE

Course website: http://bit.ly/DLSP21-web Playlist: http://bit.ly/DLSP21-YouTube Speaker: Yann LeCun Chapters 00:00:00 – Welcome to class 00:06:55 – Training methods revisited 00:08:03 – Architectural methods 00:12:00 – 1. PCA 00:18:04 – Q&A on Definitions: Labels, (un)conditional, and (un, self)supervised learning 00:25:31 – 2. Auto-encoder with Bottleneck 00:27:40 – 3. K-Means 00:34:40 – 4. Gaussian mixture model 00:41:37 – Regularized EBM 00:52:08 – Yann out of context 00:53:24 – Q&A on Norms and Posterior: when the student is thinking too far ahead 00:53:58 – 1. Unconditional regularized latent variable EBM: Sparse coding 01:06:10 – Sparse modeling on MNIST & natural patches 01:12:18 – 2. Amortized inference 01:17:02 – ISTA algorithm & RNN Encoder 01:26:56 – 3. Convolutional sparce coding 01:36:37 – 4. Video prediction: very briefly 01:39:22 – 5. VAE: an intuitive interpretation 01:48:34 – Helpful whiteboard stuff 01:52:35 – Another interpretation