GANs and Diffusion Models in Machine Learning

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Janani Ravi

2:22:15

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01 - Introduction

01 - Overview of generative models.mp4

03:53

02 - Applications of generative models.mp4

03:41

02 - 1. Getting Started with Generative Adversarial Networks

01 - Introducing GANs and diffusion models.mp4

03:08

02 - Generator and discriminator.mp4

05:36

03 - Architectural overview of a GAN.mp4

01:45

04 - Training the generator and discriminator.mp4

04:58

05 - Common problems with GANs.mp4

04:58

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

01 - Getting set up with Google Colab.mp4

03:56

02 - Loading the fashion MNIST data set.mp4

03:58

03 - The generator network.mp4

03:43

04 - The discriminator network.mp4

03:34

05 - Adversary loss functions.mp4

04:18

06 - Training the generative adversarial network.mp4

06:17

07 - Generating images using the GAN.mp4

03:50

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

01 - Overview of CNNs.mp4

04:18

02 - Transposed convolutional layer.mp4

04:22

03 - Deep Convolutional GANs.mp4

04:28

04 - Greyscale images Generator and discriminator in a Deep Convolutional GAN.mp4

06:33

05 - Greyscale images Training a Deep Convolutional GAN.mp4

06:19

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

01 - Color images Loading multichannel image data.mp4

06:22

02 - Color images Generator and discriminator in a Deep Convolutional GAN.mp4

04:58

03 - Color images Training a Deep Convolutional GAN.mp4

02:36

06 - 5. Getting Started with Diffusion Models

01 - Generative learning trilemma.mp4

04:30

02 - Introducing denoising diffusion probabilistic models.mp4

02:29

03 - How do denoising diffusion probabilistic models work.mp4

05:23

04 - Forward diffusion process.mp4

05:08

05 - Reverse diffusion process.mp4

02:51

06 - Training a diffusion model Intuition.mp4

07:20

07 - 6. Running a Diffusion Model

01 - Denoising diffusion probabilistic models Exploring implementation on GitHub.mp4

02:25

02 - Denoising diffusion probabilistic models Code overview.mp4

04:34

03 - Denoising diffusion probabilistic models Code tweaks.mp4

02:22

04 - Denoising diffusion probabilistic models Generating images.mp4

05:53

08 - Conclusion

01 - Summary and next steps.mp4

01:49

Description

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.

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Machine Learning

Janani Ravi

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Linkedin Learning

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LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.