Generative Deep Learning

Generative modeling is one of the hottest topics in AI. It is now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models such as variational autoencoders, generative adversarial networks (GANs), Transformers, normalizing flows, and diffusion models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you will understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch to generate images based on your own dataset Create autoregressive generative models, such as LSTMs for text generation and PixelCNN models for image generation Build music generation models, using Transformers and MuseGAN Explore the inner workings of state-of-the-art architectures such as StyleGAN, VQ-VAE, BERT and GPT-3 Dive into the current practical applications of generative models such as style transfer (CycleGAN, neural style transfer) and multimodal models (CLIP and DALL.E 2) for text-to-image generation Understand how generative models can help agents accomplish tasks within a reinforcement learning setting Understand how the future of generative modeling might evolve, including how businesses will need to adapt to take advantage of the new technologies