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
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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
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58sIn this video, we will discover the key concepts covered in this course. FREE ACCESS
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6m 26sAfter 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 Usage10m 56sIn this video, we will demonstrate the basic functionality of a GAN. FREE ACCESS
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4. Probability Distributions5m 55sUpon 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 Models7m 57sAfter 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 Vectors5m 9sUpon 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 GANs6m 27sAfter 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 Functions12m 12sUpon 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. Backpropagation6m 34sAfter 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 GANs7m 41sUpon 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 Methods6m 40sAfter 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 Space7m 23sUpon 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 Training4m 58sAfter 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 Project6m 11sDuring this video, you will learn how to create a GAN-based image generation project. FREE ACCESS
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15. Course Summary47sIn this video, we will summarize the key concepts covered in this course. FREE ACCESS
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