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Course10610620137 lectures

Deep Learning - Part 2

Prof. Mitesh M. Khapra · IIT Madras

Start with lecture 1 At NPTEL

Lectures

WEEK 1

  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

Week 2

  1. Independencies encoded by a Bayesian Network(Case 1: Node and it's parents) Video
  2. Independencies encoded by a Bayesian Network(Case 2: Node and it's non-parents) Video
  3. Independencies encoded by a Bayesian Network(Case 3: Node and it's descendants) Video
  4. Bayesian Networks : Formal Semantics Video
  5. I-Maps Video
  6. Markov Networks: Motivation Video
  7. Factors in Markov Network Video
  8. Local Independencies in a Markov Network Video

Week 3

  1. Joint Distributions Video
  2. The concept of a latent variable Video
  3. Restricted Boltzmann Machines Video
  4. RBMs as Stochastic Neural Networks Video
  5. Unsupervised Learning with RBMs Video

Week 4

  1. Computing the gradient of the log likelihood Video
  2. Motivation for Sampling Video
  3. Motivation for Sampling - Part - 02 Video
  4. Markov Chains Video
  5. Why de we care about Markov Chains ? Video

Week 5

  1. Setting up a Markov Chain for RBMs Video
  2. Training RBMs Using Gibbs Sampling Video
  3. Training RBMS Using Contrastive Divergence Video

Week 6

  1. Revisiting Autoencoders Video
  2. Variational Autoencoders: The Neural Network Perspective Video
  3. Variational Autoencoders: The Graphical model perspective Video

Week 7

  1. Neural Autoregressive Density Estimator Video
  2. Masked Autoencoder Density Estimator (MADE) Video

Week 8

  1. Generative Adversarial Networks - The Intuition Video
  2. Generative Adversarial Networks - Architecture Video
  3. Generative Adversarial Networks - The Math Behind it Video
  4. Generative Adversarial Networks - Some Cool Stuff and Applications Video
  5. Bringing it all together (the deep generative summary) Video

Syllabus and materials

At NPTEL, as the course publishes them.

Credits and licence

Institution
IIT Madras
Department
Computer Science and Engineering
Teacher
Prof. Mitesh M. Khapra
Level
Undergraduate
Language
English
Subjects
Computer science, Engineering
Source
NPTEL
Licence
CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
Attribution
NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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