▶︎
CS480/680 Lecture 21: Generative networks (variational autoencoders and GANs)
▶︎
Simple Explanation of AutoEncoders
▶︎
CS480/680 Lecture 19: Attention and Transformer Networks
▶︎
CS480/680 Lecture 23: Normalizing flows (Priyank Jaini)
▶︎
Variational Autoencoder - Model, ELBO, loss function and maths explained easily!
▶︎
CS480/680 Lecture 1: Course Introduction
▶︎
CS480/680 Lecture 17: Hidden Markov Models
▶︎
Computational Linear Algebra 4: Randomized SVD & Robust PCA
▶︎
Auto-Encoders (DL 22)
▶︎
Understanding Variational Autoencoders (VAEs)
▶︎
6. Monte Carlo Simulation
▶︎
CS480/680 Lecture 22: Ensemble learning (bagging and boosting)
▶︎
Reinventing Entropy | Compression is Intelligence Part 1
▶︎
CS480/680 Lecture 8: Logistic regression and generalized linear models
▶︎
Unsupervised Learning with Autoencoders | Christoph Henkelmann
▶︎
CS480/680 Lecture 18: Recurrent and recursive neural networks
▶︎
CS480/680 Lecture 12: Gaussian Processes
▶︎
Variational Autoencoders
▶︎
Lecture 21: Variational Autoencoders
▶︎