# Deep Learning - Part 2

- Institution: IIT Madras
- Department: Computer Science and Engineering
- Teacher: Prof. Mitesh M. Khapra
- Level: Undergraduate
- Language: English
- Subjects: Computer science, Engineering
- Source: [NPTEL](https://nptel.ac.in/courses/106106201)
- Licence: [CC BY-NC-SA](https://nptel.ac.in/faq) (Non-commercial use only.)
- Syllabus: [at the source](https://nptel.ac.in/courses/106106201)
- Who can watch: Tweens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

## Lectures

1. [Recap of Probability Theory](https://invisiblenetwork.ai/learn/nptel/106106201/a798a4b83788) (Video)
2. [Why are we interested in Joint Distributions](https://invisiblenetwork.ai/learn/nptel/106106201/8d7e90339649) (Video)
3. [How do we represent a joint distribution](https://invisiblenetwork.ai/learn/nptel/106106201/5530723e3799) (Video)
4. [Can we represent the joint distribution more compactly](https://invisiblenetwork.ai/learn/nptel/106106201/74f3699a0d38) (Video)
5. [Can we use a graph to represent a joint distribution](https://invisiblenetwork.ai/learn/nptel/106106201/643beb286ad8) (Video)
6. [Different types of reasoning encoded in a Bayesian Network](https://invisiblenetwork.ai/learn/nptel/106106201/5f22b34e1494) (Video)
7. [Independencies encoded by a Bayesian Network(Case 1: Node and it's parents)](https://invisiblenetwork.ai/learn/nptel/106106201/d4594a90497b) (Video)
8. [Independencies encoded by a Bayesian Network(Case 2: Node and it's non-parents)](https://invisiblenetwork.ai/learn/nptel/106106201/23f69236fb44) (Video)
9. [Independencies encoded by a Bayesian Network(Case 3: Node and it's descendants)](https://invisiblenetwork.ai/learn/nptel/106106201/453b04104f6d) (Video)
10. [Bayesian Networks : Formal Semantics](https://invisiblenetwork.ai/learn/nptel/106106201/30d562a08954) (Video)
11. [I-Maps](https://invisiblenetwork.ai/learn/nptel/106106201/e7773de18c2d) (Video)
12. [Markov Networks: Motivation](https://invisiblenetwork.ai/learn/nptel/106106201/71fa3b075bde) (Video)
13. [Factors in Markov Network](https://invisiblenetwork.ai/learn/nptel/106106201/922c0d1f2950) (Video)
14. [Local Independencies in a Markov Network](https://invisiblenetwork.ai/learn/nptel/106106201/379980a82848) (Video)
15. [Joint Distributions](https://invisiblenetwork.ai/learn/nptel/106106201/37b2ec6baccd) (Video)
16. [The concept of a latent variable](https://invisiblenetwork.ai/learn/nptel/106106201/98f2ea7d454f) (Video)
17. [Restricted Boltzmann Machines](https://invisiblenetwork.ai/learn/nptel/106106201/1eeef6148157) (Video)
18. [RBMs as Stochastic Neural Networks](https://invisiblenetwork.ai/learn/nptel/106106201/a2523b10a380) (Video)
19. [Unsupervised Learning with RBMs](https://invisiblenetwork.ai/learn/nptel/106106201/852b094f15e5) (Video)
20. [Computing the gradient of the log likelihood](https://invisiblenetwork.ai/learn/nptel/106106201/3f7bb8c28d80) (Video)
21. [Motivation for Sampling](https://invisiblenetwork.ai/learn/nptel/106106201/31990eeb8bd4) (Video)
22. [Motivation for Sampling - Part - 02](https://invisiblenetwork.ai/learn/nptel/106106201/029ef51aa02e) (Video)
23. [Markov Chains](https://invisiblenetwork.ai/learn/nptel/106106201/4a4dc909e47e) (Video)
24. [Why de we care about Markov Chains ?](https://invisiblenetwork.ai/learn/nptel/106106201/596c037779c5) (Video)
25. [Setting up a Markov Chain for RBMs](https://invisiblenetwork.ai/learn/nptel/106106201/4b3d0d20c142) (Video)
26. [Training RBMs Using Gibbs Sampling](https://invisiblenetwork.ai/learn/nptel/106106201/13ab954d1133) (Video)
27. [Training RBMS Using Contrastive Divergence](https://invisiblenetwork.ai/learn/nptel/106106201/8069310c473b) (Video)
28. [Revisiting Autoencoders](https://invisiblenetwork.ai/learn/nptel/106106201/94441b7d4f7f) (Video)
29. [Variational Autoencoders: The Neural Network Perspective](https://invisiblenetwork.ai/learn/nptel/106106201/5c410bb84f06) (Video)
30. [Variational Autoencoders: The Graphical model perspective](https://invisiblenetwork.ai/learn/nptel/106106201/0c8d1e1eb69b) (Video)
31. [Neural Autoregressive Density Estimator](https://invisiblenetwork.ai/learn/nptel/106106201/98da009573b8) (Video)
32. [Masked Autoencoder Density Estimator (MADE)](https://invisiblenetwork.ai/learn/nptel/106106201/dea93165ed51) (Video)
33. [Generative Adversarial Networks - The Intuition](https://invisiblenetwork.ai/learn/nptel/106106201/29c81897ee15) (Video)
34. [Generative Adversarial Networks - Architecture](https://invisiblenetwork.ai/learn/nptel/106106201/904adbc5fcf2) (Video)
35. [Generative Adversarial Networks - The Math Behind it](https://invisiblenetwork.ai/learn/nptel/106106201/894fc87a8ab8) (Video)
36. [Generative Adversarial Networks - Some Cool Stuff and Applications](https://invisiblenetwork.ai/learn/nptel/106106201/db120278741f) (Video)
37. [Bringing it all together (the deep generative summary)](https://invisiblenetwork.ai/learn/nptel/106106201/49602da7562c) (Video)