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Training a diffusion model: Intuition

with Janani Ravi

Explore the fundamentals of generative adversarial networks (GANs) and diffusion models, two of the most commonly used generative models in machine learning.

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Overview of generative models

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Applications of generative models

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Introducing GANs and diffusion models

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Generator and discriminator

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Architectural overview of a GAN

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Training the generator and discriminator

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Common problems with GANs

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Getting set up with Google Colab

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Loading the fashion MNIST data set

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The generator network

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The discriminator network

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Adversary loss functions

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Training the generative adversarial network

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Generating images using the GAN

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Overview of CNNs

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Transposed convolutional layer

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Deep Convolutional GANs

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Greyscale images: Generator and discriminator in a Deep Convolutional GAN

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Greyscale images: Training a Deep Convolutional GAN

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Color images: Loading multichannel image data

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Color images: Generator and discriminator in a Deep Convolutional GAN

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Color images: Training a Deep Convolutional GAN

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Generative learning trilemma

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Introducing denoising diffusion probabilistic models

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How do denoising diffusion probabilistic models work?

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Forward diffusion process

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Reverse diffusion process

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Training a diffusion model: Intuition

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Denoising diffusion probabilistic models: Exploring implementation on GitHub

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Denoising diffusion probabilistic models: Code overview

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Denoising diffusion probabilistic models: Code tweaks

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Denoising diffusion probabilistic models: Generating images

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Summary and next steps