Mathematical Foundations of Image Generation

Skillsoft issued completion badges are earned based on viewing the percentage required or receiving a passing score when assessment is required. 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.