# Inference for Latent Variable Energy-Based Models (LV-EBMs)

This lecture covers the concept of inference in latent variable energy based models (LV-EBMs) and is a part of the Deep Learning Course at NYU's Center for Data Science.

Chapters:

00:00 – Affine transformation in 2 and 3D by @LeiosOS (James Schloss)

01:21 – Thanks for sending me a Wacom graphic tablet

01:50 – *Inference* for LV EBM (we're given a model)

04:32 – Training samples: one to many mapping

13:10 – Let's simplify stuff: the unconditional case

15:56 – Untrained model manifold generation

21:15 – The Energy Function

24:51 – Indexing energy function by picking individual training samples

31:41 – The 23rd energy (U shaped)

39:27 – The 10th energy (~ shaped)

46:07 – The Free Energy (definition and the 10th example)

51:59 – The 23rd free energy

53:07 – Computing the free energy for the entire 𝒴 space

1:00:01 – That was it :)