Generative AI Models: Generating Data Using Generative Adversarial Networks
Generative AI
| Intermediate
- 11 videos | 1h 40m 18s
- Includes Assessment
- Earns a Badge
Generative adversarial networks (GANs) represent a revolutionary approach to generative modeling within the realm of artificial intelligence. Begin this course by discovering GANs, including the basic architecture of a GAN, which involves two neural networks competing in a zero-sum game - the generator and the discriminator. Next, you will explore how to construct and train a GAN using the PyTorch framework to create and train the models. You will define the generator and discriminator separately, and then kick off the model training. Finally, you will focus the deep convolutional GAN, which uses deep convolutional neural networks (CNNs) rather than regular neural networks. CNNs are optimized for working with grid-like data, such as images and these can generate better-quality images than GANs built using dense neural networks. In conclusion, this course will provide you with a strong understanding of generative adversarial networks, their architecture, and their usage scenarios.
WHAT YOU WILL LEARN
-
Discover the key concepts covered in this course
Recall how gans work
Describe the architecture of gans
Set up a virtual environment and python notebook for gan training
Load and explore the modified national institute of standards and technology (mnist) dataset
Create a generator and discriminator -
Train a gan
Provide an overview of dcgans
Load and explore the celebfaces dataset
Train a dcgan
Summarize the key concepts covered in this course
IN THIS COURSE
-
1m 59sIn this video, we will discover the key concepts covered in this course. FREE ACCESS
-
11m 36sAfter completing this video, you will be able to recall how GANs work. FREE ACCESS
-
3. Generative Adversarial Network Training7m 32sUpon completion of this video, you will be able to describe the architecture of GANs. FREE ACCESS
-
4. Setting up a Virtual Environment for GAN Training8m 4sIn this video, discover how to set up a virtual environment and Python notebook for GAN training. FREE ACCESS
-
5. Loading and Exploring the MNIST Dataset7m 28sDuring this video, you will learn how to load and explore the Modified National Institute of Standards and Technology (MNIST) dataset. FREE ACCESS
-
6. Creating a Generator and a Discriminator13m 32sFind out how to create a generator and discriminator. FREE ACCESS
-
7. Training a Generative Adversarial Network13m 51sIn this video, discover how to train a GAN. FREE ACCESS
-
8. Deep Convolutional Generative Adversarial Networks (DCGANs)11mAfter completing this video, you will be able to provide an overview of DCGANs. FREE ACCESS
-
9. Loading and Exploring the CelebFaces Dataset9m 10sLearn how to load and explore the CelebFaces dataset. FREE ACCESS
-
10. Training a DCGAN13m 16sIn this video, find out how to train a DCGAN. FREE ACCESS
-
11. Course Summary2m 50sIn this video, we will summarize the key concepts covered in this course. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.
Digital badges are yours to keep, forever.
