UG Certification in Generative Adversarial Networks (GANs)

Core Periods Modules

The UG Certification in Generative Adversarial Networks (GANs) is a structured, one-year program designed to provide undergraduate students with both foundational knowledge and hands-on experience in GANs and their applications. The program begins with an introduction to deep learning and neural networks, laying the groundwork needed to fully understand GANs. Students will then explore core GAN concepts, focusing on the interaction between generator and discriminator networks, and how they are trained. As the program progresses, students will learn advanced GAN architectures such as DCGANs, Wasserstein GANs, and CycleGANs. This will allow them to fine-tune models for specific tasks and understand their various use cases. The curriculum includes real-world applications, such as image generation, data augmentation, and medical imaging, enabling students to see how GANs are transforming industries. Additionally, the program covers the ethical challenges and responsible AI practices necessary for deploying GANs. Through hands-on projects and a capstone project, students will apply their knowledge in practical settings, equipping them with the skills required for roles in AI development, machine learning engineering, and GAN research. Upon completion, students will be well-prepared to enter the industry with specialized expertise in generative models.

1. Period 0

   Build Basic Generative Adversarial Networks (GANs)

   The Build Basic Generative Adversarial Networks (GANs) module introduces participants to the foundational concepts of GANs, including the generator and discriminator networks. Students will learn how to implement basic GAN models from scratch using popular deep learning frameworks.

2. Period 1

   Build Better Generative Adversarial Networks (GANs)

   The Build Better Generative Adversarial Networks (GANs) module advances students' understanding of GANs by focusing on improving and optimizing their performance. Participants will explore advanced techniques for stabilizing GAN training, including improved loss functions, regularization methods, and architecture modifications. The module will also cover the implementation of conditional GANs (cGANs) and Wasserstein GANs (WGANs) for enhanced output quality and better convergence. Learners will gain practical experience in fine-tuning GANs for more realistic image generation, handling mode collapse, and incorporating feedback mechanisms. The course will include hands-on projects where students will apply these techniques to real-world problems. By the end of this module, participants will be capable of building high-performing GANs for complex tasks.

3. Period 2

   Apply Generative Adversarial Networks (GANs)

   The Apply Generative Adversarial Networks (GANs) module focuses on translating theoretical knowledge into practical applications. Participants will learn to apply GANs to a variety of real-world tasks such as image generation, style transfer, and data augmentation.

4. Period 3

   Image-to-Image Translation with Pix2Pix

   The Image-to-Image Translation with Pix2Pix module focuses on teaching participants how to apply the Pix2Pix architecture for image-to-image translation tasks.

5. Period 4

   Unpaired Image-to-Image Translation With CycleGAN

   The Unpaired Image-to-Image Translation with CycleGAN module introduces participants to the CycleGAN architecture, a powerful tool for unpaired image-to-image translation.

6. Period 5

   Industry Work Experience (6 Months), 2 Electives

   The continuation of the work experience allows participants to deepen their expertise and apply their learning in more complex business scenarios. With additional electives, participants refine their understanding.