Mathematical Foundations of Image Generation

Generative AI    |    Intermediate
  • 15 videos | 1h 36m 12s
  • Includes Assessment
  • Earns a Badge
Artificial intelligence has taken the world by storm over the past few years, and it is remarkable to think that we are only starting to see the opportunities offered by AI-powered image generation. To grasp the inner workings of the technology, an understanding of the mathematical foundations of AI-powered image generation is critical. In this course, you will explore the mathematical foundations of image generation, beginning with the role of generative adversarial networks (GANs) in image generation, basic GAN usage, probability distributions, and generative models. Then you will learn about noise vectors, activation functions in GANs, and loss functions. Next, you will investigate backpropagation, conditional GANs, and style transfer methods. You will discover latent space and adversarial training. Finally, you will create your own GAN-based image generation project.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Provide an overview of gans and their role in image generation
    Demonstrate the basic functionality of a gan
    Outline probability distributions and their significance in capturing data patterns for realistic image generation
    Compare different generative models, emphasizing the mathematical principles behind gans
    Provide an overview of the mathematical concept of noise vectors and how manipulating them influences the generation of diverse images
    Describe the activation functions used in the neural networks of gans and their impact on model performance
    Provide an overview of loss functions employed in gans, such as the generator and discriminator losses
  • Describe the backpropagation process in gans and its role in optimizing the generator and discriminator networks
    Provide an overview of how conditional gans incorporate additional information into the generative process, with a focus on the underlying mathematics
    Outline the mathematical concepts behind style transfer methods, emphasizing how they enhance the artistic quality of generated images
    Provide an overview of the concept of latent space and how mathematical manipulations in this space influence the generated images
    Identify the adversarial training dynamics of gans, emphasizing the mathematical interplay between the generator and discriminator
    Create a gan-based image generation project
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 58s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 6m 26s
    After completing this video, you will be able to provide an overview of GANs and their role in image generation. FREE ACCESS
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    3.  Exploring Basic GAN Usage
    10m 56s
    In this video, we will demonstrate the basic functionality of a GAN. FREE ACCESS
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    4.  Probability Distributions
    5m 55s
    Upon completion of this video, you will be able to outline probability distributions and their significance in capturing data patterns for realistic image generation. FREE ACCESS
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    5.  Comparing Generative Models
    7m 57s
    After completing this video, you will be able to compare different generative models, emphasizing the mathematical principles behind GANs. FREE ACCESS
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    6.  Noise Vectors
    5m 9s
    Upon completion of this video, you will be able to provide an overview of the mathematical concept of noise vectors and how manipulating them influences the generation of diverse images. FREE ACCESS
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    7.  Using Activation Functions in GANs
    6m 27s
    After completing this video, you will be able to describe the activation functions used in the neural networks of GANs and their impact on model performance. FREE ACCESS
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    8.  Loss Functions
    12m 12s
    Upon completion of this video, you will be able to provide an overview of loss functions employed in GANs, such as the generator and discriminator losses. FREE ACCESS
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    9.  Backpropagation
    6m 34s
    After completing this video, you will be able to describe the backpropagation process in GANs and its role in optimizing the generator and discriminator networks. FREE ACCESS
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    10.  Conditional GANs
    7m 41s
    Upon completion of this video, you will be able to provide an overview of how conditional GANs incorporate additional information into the generative process, with a focus on the underlying mathematics. FREE ACCESS
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    11.  Style Transfer Methods
    6m 40s
    After completing this video, you will be able to outline the mathematical concepts behind style transfer methods, emphasizing how they enhance the artistic quality of generated images. FREE ACCESS
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    12.  Latent Space
    7m 23s
    Upon completion of this video, you will be able to provide an overview of the concept of latent space and how mathematical manipulations in this space influence the generated images. FREE ACCESS
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    13.  Adversarial Training
    4m 58s
    After completing this video, you will be able to identify the adversarial training dynamics of GANs, emphasizing the mathematical interplay between the generator and discriminator. FREE ACCESS
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    14.  Creating a GAN Project
    6m 11s
    During this video, you will learn how to create a GAN-based image generation project. FREE ACCESS
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    15.  Course Summary
    47s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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