Modules / Lectures
NameDownloadDownload Size
Lecture NoteDownload as zip file1.2G
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 TheoryDownload
To be verified
2Why are we interested in Joint DistributionsDownload
To be verified
3How do we represent a joint distributionDownload
To be verified
4Can we represent the joint distribution more compactlyDownload
To be verified
5Can we use a graph to represent a joint distributionDownload
To be verified
6Different types of reasoning encoded in a Bayesian NetworkDownload
To be verified
7Independencies encoded by a Bayesian Network(Case 1: Node and it's parents)Download
To be verified
8Independencies encoded by a Bayesian Network(Case 2: Node and it's non-parents)Download
To be verified
9Independencies encoded by a Bayesian Network(Case 3: Node and it's descendants)Download
To be verified
10Bayesian Networks : Formal SemanticsDownload
To be verified
11I-MapsDownload
To be verified
12Markov Networks: MotivationDownload
To be verified
13Factors in Markov NetworkDownload
To be verified
14Local Independencies in a Markov NetworkDownload
To be verified
15Joint Distributions Download
To be verified
16The concept of a latent variableDownload
To be verified
17Restricted Boltzmann MachinesDownload
To be verified
18RBMs as Stochastic Neural NetworksDownload
To be verified
19Unsupervised Learning with RBMsDownload
To be verified
20Computing the gradient of the log likelihoodDownload
To be verified
21Motivation for SamplingDownload
To be verified
22Motivation for Sampling - Part - 02Download
To be verified
23Markov ChainsDownload
To be verified
24Why de we care about Markov Chains ?Download
To be verified
25Setting up a Markov Chain for RBMsDownload
To be verified
26Training RBMs Using Gibbs SamplingDownload
To be verified
27Training RBMS Using Contrastive DivergenceDownload
To be verified
28Revisiting AutoencodersDownload
To be verified
29Variational Autoencoders: The Neural Network PerspectiveDownload
To be verified
30Variational Autoencoders: The Graphical model perspectiveDownload
To be verified
31Neural Autoregressive Density EstimatorDownload
To be verified
32Masked Autoencoder Density Estimator (MADE)Download
To be verified
33Generative Adversarial Networks - The IntuitionDownload
To be verified
34Generative Adversarial Networks - ArchitectureDownload
To be verified
35Generative Adversarial Networks - The Math Behind itDownload
To be verified
36Generative Adversarial Networks - Some Cool Stuff and ApplicationsDownload
To be verified
37Bringing it all together (the deep generative summary)Download
To be verified


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