NOC:Deep Learning - Part 2


Lecture 1 - Recap of Probability Theory


Lecture 2 - Why are we interested in Joint Distributions


Lecture 3 - How do we represent a joint distribution


Lecture 4 - Can we represent the joint distribution more compactly


Lecture 5 - Can we use a graph to represent a joint distribution


Lecture 6 - Different types of reasoning encoded in a Bayesian Network


Lecture 7 - Independencies encoded by a Bayesian Network (Case 1: Node and it's parents)


Lecture 8 - Independencies encoded by a Bayesian Network (Case 2: Node and it's non-parents)


Lecture 9 - Independencies encoded by a Bayesian Network (Case 3: Node and it's descendants)


Lecture 10 - Bayesian Networks : Formal Semantics


Lecture 11 - I-Maps


Lecture 12 - Markov Networks: Motivation


Lecture 13 - Factors in Markov Network


Lecture 14 - Local Independencies in a Markov Network


Lecture 15 - Joint Distributions


Lecture 16 - The concept of a latent variable


Lecture 17 - Restricted Boltzmann Machines


Lecture 18 - RBMs as Stochastic Neural Networks


Lecture 19 - Unsupervised Learning with RBMs


Lecture 20 - Computing the gradient of the log likelihood


Lecture 21 - Motivation for Sampling


Lecture 22 - Motivation for Sampling - Part 2


Lecture 23 - Markov Chains


Lecture 24 - Why de we care about Markov Chains ?


Lecture 25 - Setting up a Markov Chain for RBMs


Lecture 26 - Training RBMs Using Gibbs Sampling


Lecture 27 - Training RBMS Using Contrastive Divergence


Lecture 28 - Revisiting Autoencoders


Lecture 29 - Variational Autoencoders: The Neural Network Perspective


Lecture 30 - Variational Autoencoders: The Graphical model perspective


Lecture 31 - Neural Autoregressive Density Estimator


Lecture 32 - Masked Autoencoder Density Estimator (MADE)


Lecture 33 - Generative Adversarial Networks - The Intuition


Lecture 34 - Generative Adversarial Networks - Architecture


Lecture 35 - Generative Adversarial Networks - The Math Behind it


Lecture 36 - Generative Adversarial Networks - Some Cool Stuff and Applications


Lecture 37 - Bringing it all together (the deep generative summary)