Build Advanced Generative Adversarial Networks

In this course, you will: - Assess the challenges of evaluating GANs and compare different generative models - Use the Fréchet Inception Distance (FID) method to evaluate the fidelity and diversity of GANs - Identify sources of bias and the ways to detect it in GANs - Learn and implement the techniques associated with the state-of-the-art StyleGANs The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more. Build a comprehensive knowledge base and gain hands-on experience in GANs. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs. This Specialization provides an accessible pathway for all levels of learners looking to break into the GANs space or apply GANs to their own projects, even without prior familiarity with advanced math and machine learning research

• Assess challenges in evaluating GANs
• Compare different generative models using Fréchet Inception Distance
• Identify sources of bias and detection methods in GANs
• Learn and implement advanced StyleGANs techniques
• Build a comprehensive knowledge base on generative models

• Basic calculus, linear algebra, and statistics knowledge
• Basic understanding of AI, deep learning, and convolutional neural networks
• Intermediate Python skills and experience with deep learning frameworks (TensorFlow / PyTorch)