Deep Learning606 Videos

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10
01:11:52

11-785, Fall 22 Lecture 23: Generative Adversarial Networks (Part 1)

The first lecture on GANs was the first lecture of the semester on Generative models. We have seen discriminator models which Model the conditional distribution. Discriminative models find and it aims to find a decision boundary which separates this data from this set of data. So in in in generator models your aim is to just find the distribution of the data and not just to find the boundary.
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9
01:28:03

11-785, Fall 22 Lecture 20: Representation Learning

Today we're going to be talking about what neural networks learn. So what we've seen so far is this neural networks are universal approximators. They can model any Boolean category color real value function.
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01:25:25

11-785, Fall 22 Lecture 21: Variational Auto Encoders

We're going to start our new sequence of lectures on neural networks for modeling distributions. So what we've seen so far is that neural networks are universal approximators. They can model Boolean functions, classification functions.
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19
01:34:01

11-785 Deep Learning Recitation 11: Transformers Part 1

This is the first part of the transformers recitation where we will quote from scratch. This one will be more about the basics of transformers and how we coded so that for the future recitations we can just go over what researchers in the community have done and what the architectures are.