Generative Adversarial Networks (GANs) Professional

Core Periods Modules

The Generative Adversarial Networks (GANs) Professional program offers a comprehensive learning experience, structured to provide participants with both theoretical knowledge and hands-on expertise in GANs and their applications. The first phase covers the fundamentals of deep learning and neural networks, establishing the groundwork needed to understand GANs. Participants will then dive into the core concepts of GANs, focusing on the interplay between the generator and discriminator networks and training dynamics. The program progresses to more advanced GAN architectures, including DCGANs, Wasserstein GANs, and CycleGANs, equipping learners with the skills to fine-tune models for specific tasks. With real-world applications such as image generation, data augmentation, and medical imaging, participants will gain practical insights into how GANs are revolutionizing various industries. Ethical considerations, challenges, and solutions related to GAN deployment are also covered, ensuring responsible AI development. Hands-on projects and a capstone will help learners apply their knowledge in practical settings, preparing them for careers as AI Engineers, Machine Learning Specialists, or GAN Researchers.

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.