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


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