"Hands-On Generative AI with TensorFlow 2.0" by Joseph Babcock and Rajalingappaa Shanmugamani: This book provides an introduction to generative AI, deep learning, and TensorFlow 2.0
"Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play" by David Foster: This book focuses on generative models for art and music, including topics such as variational autoencoders
"The Deep Learning Book" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: This comprehensive textbook covers a wide range of topics in deep learning, including generative models such as GANs and variational autoencoders.
"Generative Models: An Overview" by David Blei: This paper provides an overview of generative models and their applications in machine learning, including topics such as Bayesian networks
"Auto-Encoding Variational Bayes" by Diederik P. Kingma and Max Welling: This paper introduced the variational autoencoder (VAE) model, which is a type of generative model that learns a probabilistic latent representation of data.
"Generative Adversarial Networks" by Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio: This seminal paper introduced the concept of GANs and provides a detailed explanation of their architecture and training procedure.
"The Unreasonable Effectiveness of Deep Learning" by Yann LeCun, Yoshua Bengio, and Geoffrey Hinton: This paper provides an overview of the deep learning revolution
"Generative Modeling and Inverse Graphics in Vision" by William T. Freeman and Antonio Torralba: This paper discusses the use of generative models in computer vision tasks such as image synthesis and 3D reconstruction.
"GANs Beyond the Hype: Insights from Anomaly Detection" by Ananya Harsh Jha, Dinh Phung, and Svetha Venkatesh: This paper discusses the use of GANs for anomaly detection