Deep Learning Architectures — Deep Generative Modeling | Computer Science Online Tutorial

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Deep Learning Architectures

A self-paced online tutorial session on deep generative modeling, part of the Deep Learning Architectures track. Work through the topics below, then reach out any time if you'd like a live walkthrough.

Estimated session length: 9 hours Format: Online, self-paced

What this tutorial covers

  • 01Types
  • 02Autoregressive models
  • 03Autoregressive Models Parameterized by Neural Networks
  • 04Deep Generative Autoregressive Model: an example Flow based models
  • 05Flows for Continuous Random Variables
  • 06Change of Variables for Deep Generative Modeling
  • 07Building Blocks of RealNVP - example
  • 08Flows for Discrete Random Variables
  • 09Flows in R or Maybe Rather in Z
  • 10Integer Discrete Flows
  • 11Case study using Deep generative modeling

How to use this session

Read through each topic in order — they build on one another. Keep a notes document open and try to restate each concept in your own words before moving to the next one; that single habit does more for retention than re-reading ever will.

If any topic above needs a deeper explanation, a worked example, or a live session with an instructor, get in touch using the details below and we'll set up a time that works for you.

Need help with this topic?

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