Advances in Deep Generative Models by Balasubramaniam S (.ePUB)+
File Size: 25.7 MB
Advances in Deep Generative Models for Healthcare and Medical Applications by Balasubramaniam S, Seifedine Kadry
Requirements: .ePUB, .PDF reader, 25.7 MB
Overview: This book provides a platform for research on Deep Generative Models, with an emphasis on its healthcare applications. The book addresses the unanswered questions that stop these approaches from making a huge difference in real-world clinical practice. The goal of this book is to bring together a wide range of methodologies which are using generative models in health care-related contexts. The book leverages the recent methodological advancements in Deep Generative Models to address critical health-care challenges across all data-types, paving the way for their practical integration into the healthcare system and elevate their impact on the future of healthcare. Recent years have seen numerous groundbreaking achievements in Generative AI, particularly in the areas of computer vision, voice processing, and natural language processing. High quality synthetic human faces, artworks, and cohesive essays on multiple subjects can be produced with generative adversarial networks and diffusion models. Deep Generative Models (DGMs) represent a category of Machine Learning methods that utilize deep neural networks to model and learn the probability distributions of complex data. Unlike discriminative models, which focus on predicting a target label based on input data, DGMs aim to understand the structure and distribution of the data itself.
Genre: Non-Fiction > Tech & Devices
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