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 59s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 11m 36s
    After completing this video, you will be able to recall how GANs work. FREE ACCESS
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    3.  Generative Adversarial Network Training
    7m 32s
    Upon completion of this video, you will be able to describe the architecture of GANs. FREE ACCESS
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    4.  Setting up a Virtual Environment for GAN Training
    8m 4s
    In this video, discover how to set up a virtual environment and Python notebook for GAN training. FREE ACCESS
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    5.  Loading and Exploring the MNIST Dataset
    7m 28s
    During this video, you will learn how to load and explore the Modified National Institute of Standards and Technology (MNIST) dataset. FREE ACCESS
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    6.  Creating a Generator and a Discriminator
    13m 32s
    Find out how to create a generator and discriminator. FREE ACCESS
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    7.  Training a Generative Adversarial Network
    13m 51s
    In this video, discover how to train a GAN. FREE ACCESS
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    8.  Deep Convolutional Generative Adversarial Networks (DCGANs)
    11m
    After completing this video, you will be able to provide an overview of DCGANs. FREE ACCESS
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    9.  Loading and Exploring the CelebFaces Dataset
    9m 10s
    Learn how to load and explore the CelebFaces dataset. FREE ACCESS
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    10.  Training a DCGAN
    13m 16s
    In this video, find out how to train a DCGAN. FREE ACCESS
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    11.  Course Summary
    2m 50s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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