| Name | Download | Download Size |
|---|---|---|
| Lecture Note | Download as zip file | 1.2G |
| Module Name | Download |
|---|---|
| noc19_cs18-assessmentid-36 | noc19_cs18-assessmentid-36 |
| noc19_cs18-assessmentid-50 | noc19_cs18-assessmentid-50 |
| noc19_cs18-assessmentid-53 | noc19_cs18-assessmentid-53 |
| noc19_cs18-assessmentid-67 | noc19_cs18-assessmentid-67 |
| noc19_cs18-assessmentid-71 | noc19_cs18-assessmentid-71 |
| noc19_cs18-assessmentid-73 | noc19_cs18-assessmentid-73 |
| noc19_cs18-assessmentid-78 | noc19_cs18-assessmentid-78 |
| noc19_cs18-assessmentid-84 | noc19_cs18-assessmentid-84 |
| Sl.No | Chapter Name | MP4 Download |
|---|---|---|
| 1 | Recap of Probability Theory | Download |
| 2 | Why are we interested in Joint Distributions | Download |
| 3 | How do we represent a joint distribution | Download |
| 4 | Can we represent the joint distribution more compactly | Download |
| 5 | Can we use a graph to represent a joint distribution | Download |
| 6 | Different types of reasoning encoded in a Bayesian Network | Download |
| 7 | Independencies encoded by a Bayesian Network(Case 1: Node and it's parents) | Download |
| 8 | Independencies encoded by a Bayesian Network(Case 2: Node and it's non-parents) | Download |
| 9 | Independencies encoded by a Bayesian Network(Case 3: Node and it's descendants) | Download |
| 10 | Bayesian Networks : Formal Semantics | Download |
| 11 | I-Maps | Download |
| 12 | Markov Networks: Motivation | Download |
| 13 | Factors in Markov Network | Download |
| 14 | Local Independencies in a Markov Network | Download |
| 15 | Joint Distributions | Download |
| 16 | The concept of a latent variable | Download |
| 17 | Restricted Boltzmann Machines | Download |
| 18 | RBMs as Stochastic Neural Networks | Download |
| 19 | Unsupervised Learning with RBMs | Download |
| 20 | Computing the gradient of the log likelihood | Download |
| 21 | Motivation for Sampling | Download |
| 22 | Motivation for Sampling - Part - 02 | Download |
| 23 | Markov Chains | Download |
| 24 | Why de we care about Markov Chains ? | Download |
| 25 | Setting up a Markov Chain for RBMs | Download |
| 26 | Training RBMs Using Gibbs Sampling | Download |
| 27 | Training RBMS Using Contrastive Divergence | Download |
| 28 | Revisiting Autoencoders | Download |
| 29 | Variational Autoencoders: The Neural Network Perspective | Download |
| 30 | Variational Autoencoders: The Graphical model perspective | Download |
| 31 | Neural Autoregressive Density Estimator | Download |
| 32 | Masked Autoencoder Density Estimator (MADE) | Download |
| 33 | Generative Adversarial Networks - The Intuition | Download |
| 34 | Generative Adversarial Networks - Architecture | Download |
| 35 | Generative Adversarial Networks - The Math Behind it | Download |
| 36 | Generative Adversarial Networks - Some Cool Stuff and Applications | Download |
| 37 | Bringing it all together (the deep generative summary) | Download |
| Sl.No | Chapter Name | English |
|---|---|---|
| 1 | Recap of Probability Theory | Download Verified |
| 2 | Why are we interested in Joint Distributions | Download Verified |
| 3 | How do we represent a joint distribution | Download Verified |
| 4 | Can we represent the joint distribution more compactly | Download Verified |
| 5 | Can we use a graph to represent a joint distribution | Download Verified |
| 6 | Different types of reasoning encoded in a Bayesian Network | Download Verified |
| 7 | Independencies encoded by a Bayesian Network(Case 1: Node and it's parents) | Download Verified |
| 8 | Independencies encoded by a Bayesian Network(Case 2: Node and it's non-parents) | Download Verified |
| 9 | Independencies encoded by a Bayesian Network(Case 3: Node and it's descendants) | Download Verified |
| 10 | Bayesian Networks : Formal Semantics | Download Verified |
| 11 | I-Maps | Download Verified |
| 12 | Markov Networks: Motivation | Download Verified |
| 13 | Factors in Markov Network | Download Verified |
| 14 | Local Independencies in a Markov Network | Download Verified |
| 15 | Joint Distributions | Download Verified |
| 16 | The concept of a latent variable | Download Verified |
| 17 | Restricted Boltzmann Machines | Download Verified |
| 18 | RBMs as Stochastic Neural Networks | Download Verified |
| 19 | Unsupervised Learning with RBMs | Download Verified |
| 20 | Computing the gradient of the log likelihood | Download Verified |
| 21 | Motivation for Sampling | Download Verified |
| 22 | Motivation for Sampling - Part - 02 | Download Verified |
| 23 | Markov Chains | Download Verified |
| 24 | Why de we care about Markov Chains ? | Download Verified |
| 25 | Setting up a Markov Chain for RBMs | Download Verified |
| 26 | Training RBMs Using Gibbs Sampling | Download Verified |
| 27 | Training RBMS Using Contrastive Divergence | Download Verified |
| 28 | Revisiting Autoencoders | Download Verified |
| 29 | Variational Autoencoders: The Neural Network Perspective | Download Verified |
| 30 | Variational Autoencoders: The Graphical model perspective | Download Verified |
| 31 | Neural Autoregressive Density Estimator | Download Verified |
| 32 | Masked Autoencoder Density Estimator (MADE) | Download Verified |
| 33 | Generative Adversarial Networks - The Intuition | Download Verified |
| 34 | Generative Adversarial Networks - Architecture | Download Verified |
| 35 | Generative Adversarial Networks - The Math Behind it | Download Verified |
| 36 | Generative Adversarial Networks - Some Cool Stuff and Applications | Download Verified |
| 37 | Bringing it all together (the deep generative summary) | Download Verified |
| Sl.No | Language | Book link |
|---|---|---|
| 1 | English | Not Available |
| 2 | Bengali | Not Available |
| 3 | Gujarati | Not Available |
| 4 | Hindi | Not Available |
| 5 | Kannada | Not Available |
| 6 | Malayalam | Not Available |
| 7 | Marathi | Not Available |
| 8 | Tamil | Not Available |
| 9 | Telugu | Not Available |