Building Generative Adversarial Networks (GANs) with PyTorch

Generative AI is reshaping the technology landscape, and Generative Adversarial Networks (GANs) are at the forefront of this revolution. Understanding how these dual-network systems compete and cooperate is essential for anyone entering the field of deep learning. This text-based course guides you from the fundamental mathematical intuition of GANs to writing clean, functional code. You will transition from understanding basic probability distributions to implementing classic architectures that can generate entirely new, realistic synthetic data. What you'll learn: - Understand the fundamental architecture of GANs, including the generator, the discriminator, and the minimax game formulation. - Implement basic GANs and Deep Convolutional GANs (DCGANs) using modern PyTorch conventions. - Build Conditional GANs (CGANs) to control the specific categories of data your model generates. - Analyze and troubleshoot common GAN training challenges such as mode collapse and vanishing gradients. - Apply basic evaluation metrics and modern stabilization techniques to assess the quality of generated outputs. The course begins with core definitions and the foundational theory of generative modeling before moving into step-by-step code walkthroughs. You will read detailed explanations of network design, loss functions, and training loops designed to solidify your conceptual understanding. This course is designed for beginners in deep learning who have a basic familiarity with Python and neural networks, requiring no prior experience with generative models. Start reading today to build your first generative models from scratch.