Modules / Lectures
Module NameDownload
noc19_cs18-assessmentid-36noc19_cs18-assessmentid-36
noc19_cs18-assessmentid-50noc19_cs18-assessmentid-50
noc19_cs18-assessmentid-53noc19_cs18-assessmentid-53
noc19_cs18-assessmentid-67noc19_cs18-assessmentid-67
noc19_cs18-assessmentid-71noc19_cs18-assessmentid-71
noc19_cs18-assessmentid-73noc19_cs18-assessmentid-73
noc19_cs18-assessmentid-78noc19_cs18-assessmentid-78
noc19_cs18-assessmentid-84noc19_cs18-assessmentid-84

Sl.No Chapter Name English
1Recap of Probability TheoryPDF unavailable
2Why are we interested in Joint DistributionsPDF unavailable
3How do we represent a joint distributionPDF unavailable
4Can we represent the joint distribution more compactlyPDF unavailable
5Can we use a graph to represent a joint distributionPDF unavailable
6Different types of reasoning encoded in a Bayesian NetworkPDF unavailable
7Independencies encoded by a Bayesian Network(Case 1: Node and it's parents)PDF unavailable
8Independencies encoded by a Bayesian Network(Case 2: Node and it's non-parents)PDF unavailable
9Independencies encoded by a Bayesian Network(Case 3: Node and it's descendants)PDF unavailable
10Bayesian Networks : Formal SemanticsPDF unavailable
11I-MapsPDF unavailable
12Markov Networks: MotivationPDF unavailable
13Factors in Markov NetworkPDF unavailable
14Local Independencies in a Markov NetworkPDF unavailable
15Joint Distributions PDF unavailable
16The concept of a latent variablePDF unavailable
17Restricted Boltzmann MachinesPDF unavailable
18RBMs as Stochastic Neural NetworksPDF unavailable
19Unsupervised Learning with RBMsPDF unavailable
20Computing the gradient of the log likelihoodPDF unavailable
21Motivation for SamplingPDF unavailable
22Motivation for Sampling - Part - 02PDF unavailable
23Markov ChainsPDF unavailable
24Why de we care about Markov Chains ?PDF unavailable
25Setting up a Markov Chain for RBMsPDF unavailable
26Training RBMs Using Gibbs SamplingPDF unavailable
27Training RBMS Using Contrastive DivergencePDF unavailable
28Revisiting AutoencodersPDF unavailable
29Variational Autoencoders: The Neural Network PerspectivePDF unavailable
30Variational Autoencoders: The Graphical model perspectivePDF unavailable
31Neural Autoregressive Density EstimatorPDF unavailable
32Masked Autoencoder Density Estimator (MADE)PDF unavailable
33Generative Adversarial Networks - The IntuitionPDF unavailable
34Generative Adversarial Networks - ArchitecturePDF unavailable
35Generative Adversarial Networks - The Math Behind itPDF unavailable
36Generative Adversarial Networks - Some Cool Stuff and ApplicationsPDF unavailable
37Bringing it all together (the deep generative summary)PDF unavailable


Sl.No Language Book link
1EnglishNot Available
2BengaliNot Available
3GujaratiNot Available
4HindiNot Available
5KannadaNot Available
6MalayalamNot Available
7MarathiNot Available
8TamilNot Available
9TeluguNot Available