APX AI
Online
Construct sophisticated Generative Adversarial Networks (GANs). This course covers advanced architectures, tackles complex training challenges like mode collapse, and details rigorous evaluation metrics. Implement cutting-edge GAN variants and understand the theoretical underpinnings driving modern generative models. Suitable for engineers and researchers seeking to push the boundaries of generative AI.
Prerequisites Intermediate ML, Python required
Level:
Advanced Architectures
Implement and analyze sophisticated GAN architectures like StyleGAN, BigGAN, and CycleGAN.
Training Stability
Apply techniques such as Wasserstein loss, gradient penalties, and spectral normalization to stabilize GAN training.
Conditional Generation
Build and train conditional GANs (cGANs) and information-maximizing GANs (InfoGAN) for controlled data synthesis.
Model Evaluation
Utilize and interpret advanced metrics like Fréchet Inception Distance (FID) and Inception Score (IS) for GAN performance assessment.
Theoretical Understanding
Grasp the mathematical concepts and theoretical justifications behind advanced GAN methods.
Implementation Proficiency
Develop the skills to implement, train, and debug complex GAN models using standard deep learning frameworks.