Final Exam: Image Generation with AI

Intermediate
  • 1 video | 32s
  • Includes Assessment
  • Earns a Badge
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Final Exam: Image Generation with AI will test your knowledge and application of the topics presented throughout the Image Generation with AI journey.

WHAT YOU WILL LEARN

  • Provide an overview of image generation with ai and its significance
    provide an overview of popular generative ai models
    outline the various methods used in ai-powered image generation
    describe various ways generative ai can be leveraged to create content
    outline the key components of an ai-powered image generation pipeline
    discuss real-world use cases for generative ai across industries, from art to healthcare
    provide an overview of key ethical considerations surrounding ai-generated images
    discuss ai's influence on photography, art, and media
    describe the implications of ai-driven visual content for businesses and society
    provide an overview of the foundational concepts of variational autoencoders (vaes) and their role in image generation
    outline the principles behind generative adversarial networks (gans) and their significance in ai-generated visuals
    describe various gan architectures, including dcgan, wgan, cyclegan, and stylegan
    outline the training process of a basic gan
    outline auto-regressive models and their pixel-level image generation approach
    provide an overview of auto-regressive models and how they compare to other image generation techniques
    outline the concept of diffusion models and their innovative approach to image generation
    discuss practical use cases of diffusion models in generating diverse visual content
    describe the advantages and limitations of various image generation frameworks
    demonstrate the basic functionality of a generative adversarial network (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
    describe 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
    discuss how conditional gans incorporate additional information into the generative process, with a focus on the underlying mathematics
    outline 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
    demonstrate how to create a gan-based image generation project
    outline core principles of variational autoencoders (vaes)
    demonstrate how to build new and distinctive images with keras and customize vae parameters in keras to achieve specific creative outcomes tailored to your vision
    demonstrate how to create an autoencoder in keras
    discuss image manipulation with latent space variables using keras
    discuss image editing using vaes, such as compression and color adjustments
    demonstrate methods for training vaes
    demonstrate how to get started with keras and leverage its functionalities for vae-based image manipulation
    discuss the popular tool keras and how it can be used to implement vaes in image editing and creation
    discuss how stable diffusion enhances the realism and detail of ai-generated images
    describe how to implement stable diffusion with keras
    discuss stable diffusion models and their respective uses
    outline how stable diffusion enables the creation of high-resolution images
    describe variations of diffusion models and their uses
    demonstrate methods for training vaes
    describe inpainting and how it’s used in stable diffusion
    discuss stable diffusion as an advanced ai technique for image generation
    discuss various applications for diffusion models in keras
    demonstrate how to utilize image generation and image inpainting using stable diffusion and keras
    demonstrate how to construct and train a basic stable diffusion model and generate images using keras
    provide an overview of ways to fine-tune models to achieve specific creative outcomes in image generation
    demonstrate how to denoise implicit diffusion models with keras
    outline the advantages of high resolution image generation in keras
    provide an overview of how high resolution image generation in keras works

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