English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 2h 22m | 1.75 GB
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.
Table of Contents
Introduction
1 Overview of generative models
2 Applications of generative models
Getting Started with Generative Adversarial Networks
3 Introducing GANs and diffusion models
4 Generator and discriminator
5 Architectural overview of a GAN
6 Training the generator and discriminator
7 Common problems with GANs
Building a GAN Using a Dense Neural Network
8 Getting set up with Google Colab
9 Loading the fashion MNIST data set
10 The generator network
11 The discriminator network
12 Adversary loss functions
13 Training the generative adversarial network
14 Generating images using the GAN
Building a GAN Using a Deep Convolutional Network
15 Overview of CNNs
16 Transposed convolutional layer
17 Deep Convolutional GANs
18 Greyscale images Generator and discriminator in a Deep Convolutional GAN
19 Greyscale images Training a Deep Convolutional GAN
Training a Deep Convolutional GAN on Multichannel Images
20 Color images Loading multichannel image data
21 Color images Generator and discriminator in a Deep Convolutional GAN
22 Color images Training a Deep Convolutional GAN
Getting Started with Diffusion Models
23 Generative learning trilemma
24 Introducing denoising diffusion probabilistic models
25 How do denoising diffusion probabilistic models work
26 Forward diffusion process
27 Reverse diffusion process
28 Training a diffusion model Intuition
Running a Diffusion Model
29 Denoising diffusion probabilistic models Exploring implementation on GitHub
30 Denoising diffusion probabilistic models Code overview
31 Denoising diffusion probabilistic models Code tweaks
32 Denoising diffusion probabilistic models Generating images
Conclusion
33 Summary and next steps
Resolve the captcha to access the links!