Building Generative Models

Get an introduction to building Generative Adversarial Networks, meet your instructor, and preview key topics covered in the course.

Explore the shift from data recognition to creation in AI through generative models, their applications, differences from classifiers, and crucial ethical considerations.

Learn how generative AI uses latent spaces and data distributions to transform random noise into structured, creative outputs by mapping simple vectors to complex data like images.

Learn to build a simple generator network in PyTorch that transforms random noise into images, which is a core foundation for Generative Adversarial Networks (GANs) and creative AI models.

Discover how Generative Adversarial Networks use competition between a Generator and Discriminator to create realistic synthetic data through adversarial training.

Learn how to build, train, and evaluate a discriminator in PyTorch as part of a GAN, mastering adversarial training, data preparation, and model evaluation techniques.

Explore GANs: two neural networks, a generator and a discriminator, compete to create realistic data, driving advances in creative AI like image, art, and text generation.

Learn step-by-step GAN training with PyTorch by building, training, and analyzing generator and discriminator models to create realistic images and understand adversarial processes.

Learn to overcome GAN training challenges like mode collapse and instability using techniques such as label smoothing, batch normalization, and LeakyReLU for stable, diverse outputs.

Learn how label smoothing and feature matching stabilize GAN training in PyTorch, alleviating common issues like instability and mode collapse for more reliable, diverse outputs.

Learn to build Deep Convolutional GANs (DCGANs) using convolutional layers, transposed/strided convolutions, and stable training to generate high-quality, realistic images.

Build and train a DCGAN in PyTorch to create realistic images from random noise, mastering adversarial training, architecture, and techniques for creative image generation.

Learn how Conditional GANs generate specific images by combining labels with noise, enabling control for tasks like data augmentation, text-to-image, and image-to-image translation.

Learn to build, train, and use a conditional GAN to generate images of specific classes, gaining control over generative models by conditioning on class labels.

Learn how diffusion models generate images by adding and removing noise in steps, using neural networks, and why they're stable and effective for generative AI.

Discover how the U-Net architecture enables diffusion models to create images with both structure and fine detail, using encoder-decoder paths, skip connections, and time embedding conditioning.

Learn to build, train, and implement a basic diffusion model from scratch, mastering forward noising, U-Net architecture, and image generation through denoising steps.

Learn how diffusion models generate images by reverse diffusion, denoising random noise step-by-step, and explore the trade-off between generation speed and image quality.

Explore the key differences between GANs and diffusion models, speed versus image quality, and learn to choose the right AI tool for any creative image generation task.