Build GANs and Diffusion Models with TensorFlow and PyTorch

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If you’re looking for a crash course in generative modeling, this course was made for you. Generative adversarial networks (GANs) and diffusion models are some of the most important components of machine learning infrastructure. Join instructor Janani Ravi to find out more about how to get started building GANs with both dense neural as well as deep convolutional networks. Javani shows you the basics of how to train a deep convolutional GAN on multichannel images. Along the way, she gives you tips on how to get up and running with GANs using TensorFlow and diffusion models using PyTorch.

01 - Introduction

01 - Overview of generative models

02 - Applications of generative models

02 - 1. Getting Started with Generative Adversarial Networks

01 - Introducing GANs and diffusion models

02 - Generator and discriminator

03 - Architectural overview of a GAN

04 - Training the generator and discriminator

05 - Common problems with GANs

03 - 2. Building a GAN Using a Dense Neural Network

01 - Getting set up with Google Colab

02 - Loading the fashion MNIST data set

03 - The generator network

04 - The discriminator network

05 - Adversary loss functions

06 - Training the generative adversarial network

07 - Generating images using the GAN

04 - 3. Building a GAN Using a Deep Convolutional Network

01 - Overview of CNNs

02 - Transposed convolutional layer

03 - Deep Convolutional GANs

04 - Greyscale images Generator and discriminator in a Deep Convolutional GAN

05 - Greyscale images Training a Deep Convolutional GAN

05 - 4. Training a Deep Convolutional GAN on Multichannel Images

01 - Color images Loading multichannel image data

02 - Color images Generator and discriminator in a Deep Convolutional GAN

03 - Color images Training a Deep Convolutional GAN

06 - 5. Getting Started with Diffusion Models

01 - Generative learning trilemma

02 - Introducing denoising diffusion probabilistic models

03 - How do denoising diffusion probabilistic models work

04 - Forward diffusion process

05 - Reverse diffusion process

06 - Training a diffusion model Intuition

07 - 6. Running a Diffusion Model

01 - Denoising diffusion probabilistic models Exploring implementation on GitHub

02 - Denoising diffusion probabilistic models Code overview

03 - Denoising diffusion probabilistic models Code tweaks

04 - Denoising diffusion probabilistic models Generating images

08 - Conclusion

01 - Summary and next steps

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تولید کننده: LinkedIn Learning (Lynda)