Learn · Deep Learning - Part 2

Lecture 10 of 37Week 2

Bayesian Networks : Formal Semantics

Prof. Mitesh M. Khapra · IIT Madras

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From
Deep Learning - Part 2, IIT Madras (at NPTEL)
Licence
CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
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NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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All the lectures

  1. Recap of Probability Theory Video
  2. Why are we interested in Joint Distributions Video
  3. How do we represent a joint distribution Video
  4. Can we represent the joint distribution more compactly Video
  5. Can we use a graph to represent a joint distribution Video
  6. Different types of reasoning encoded in a Bayesian Network Video
  7. Independencies encoded by a Bayesian Network(Case 1: Node and it's parents) Video
  8. Independencies encoded by a Bayesian Network(Case 2: Node and it's non-parents) Video
  9. Independencies encoded by a Bayesian Network(Case 3: Node and it's descendants) Video
  10. Bayesian Networks : Formal Semantics Video
  11. I-Maps Video
  12. Markov Networks: Motivation Video
  13. Factors in Markov Network Video
  14. Local Independencies in a Markov Network Video
  15. Joint Distributions Video
  16. The concept of a latent variable Video
  17. Restricted Boltzmann Machines Video
  18. RBMs as Stochastic Neural Networks Video
  19. Unsupervised Learning with RBMs Video
  20. Computing the gradient of the log likelihood Video
  21. Motivation for Sampling Video
  22. Motivation for Sampling - Part - 02 Video
  23. Markov Chains Video
  24. Why de we care about Markov Chains ? Video
  25. Setting up a Markov Chain for RBMs Video
  26. Training RBMs Using Gibbs Sampling Video
  27. Training RBMS Using Contrastive Divergence Video
  28. Revisiting Autoencoders Video
  29. Variational Autoencoders: The Neural Network Perspective Video
  30. Variational Autoencoders: The Graphical model perspective Video
  31. Neural Autoregressive Density Estimator Video
  32. Masked Autoencoder Density Estimator (MADE) Video
  33. Generative Adversarial Networks - The Intuition Video
  34. Generative Adversarial Networks - Architecture Video
  35. Generative Adversarial Networks - The Math Behind it Video
  36. Generative Adversarial Networks - Some Cool Stuff and Applications Video
  37. Bringing it all together (the deep generative summary) Video