In this course, you will delve into the world of Generative Adversarial Networks (GANs) and their diverse applications. From data augmentation and privacy to image-to-image translation using models like Pix2Pix and CycleGAN, you will gain hands-on experience and explore advanced concepts in an accessible manner.
Throughout the course, you will analyze the impact of GANs on social implications such as bias in machine learning and privacy preservation. By leveraging PyTorch, you will gain practical skills to train your own GAN models and create images.
Whether you are new to GANs or seeking to apply them to your own projects, this course offers a comprehensive learning path while making advanced concepts in math and machine learning research accessible to learners of all levels.
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This course consists of three modules, each offering in-depth exploration of GAN applications, including data augmentation, privacy, image-to-image translation using models like Pix2Pix and CycleGAN, and social implications of GANs.
Welcome to Course 3, where you’ll delve into GAN applications, including data augmentation, privacy, and anonymity. You’ll also have the opportunity to explore optional topics related to GANs.
In Week 2, you will explore image-to-image translation with Pix2Pix. This module covers the Pix2Pix model, including its components and advancements, providing you with a deep understanding of this GAN application.
Week 3 focuses on unpaired translation with CycleGAN. You will delve into the CycleGAN model, understanding its components, applications, and variants, and explore optional topics related to CycleGAN.
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