Learn to use generative AI techniques to create novel text, images, audio, and even music with this practical, hands-on book. Readers will understand how state-of-the-art generative models work, how to fine-tune and adapt them to their needs, and how to combine existing building blocks to create new models and creative applications in different domains.
This go-to book introduces theoretical concepts followed by guided practical applications, with extensive code samples and easy-to-understand illustrations. You'll learn how to use open source libraries to utilize transformers and diffusion models, conduct code exploration, and study several existing projects to help guide your work.
Build and customize models that can generate text and images
Explore trade-offs between using a pretrained model and fine-tuning your own model
Create and utilize models that can generate, edit, and modify images in any style
Customize transformers and diffusion models for multiple creative purposes
Train models that can reflect your own unique style
Sprache
Verlagsort
Produkt-Hinweis
Broschur/Paperback
Klebebindung
Maße
Höhe: 231 mm
Breite: 187 mm
Dicke: 21 mm
Gewicht
ISBN-13
978-1-0981-4924-6 (9781098149246)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Omar Sanseviero is the Lead of Developer Advocacy Engineering at Hugging Face, where he builds collaborations with different libraries in the ML Ecosystem. Omar has extensive engineering experience working in Google in Google Assistant and TensorFlow Graphics. Omar's work at Hugging Face is at the intersection of community, engineering, and product, allowing him to have a horizontal understanding of the ML ecosystem and trends. Pedro Cuenca is a Machine Learning Engineer at Hugging Face working on diffusion software, models, and applications. He has 20+ years of software development experience in fields like Internet applications (in Spain, he helped create the first interactive educational portal, the first book store, and the first free ISP) and, more recently, iOS. As a co-founder and CTO of LateNiteSoft, he worked on the technology behind Camera+, a successful iPhone photography app. He created deep-learning models for tasks such as photography enhancement and super-resolution. He was also involved in the development and operations behind dalle-mini. He brings a practical vision of integrating AI research into real-world services and the challenges and optimizations involved. Apolinario Passos is a Machine Learning Art Engineer at Hugging Face working across different teams on multiple machine learning for art and creativity use-cases. Apolinario has 10+ years of professional and artistic experience, alternating between holding art exhibitions, coding, and product management, having been a Head of Product in World Data Lab. Apolinario aims to ensure that the ML ecosystem supports and makes sense for artistic use cases.Jonathan Whitaker is a data scientist and deep learning researcher focused on generative modelling. He created and taught the 'AIAIART' course and is working on a new version called 'The Generative Landscape' which covers many of the topics this book hopes to address. He also wrote the Hugging Face diffusion models class and is working with Jeremy Howard on the ongoing FastAI course -'Stable Diffusion from the Foundations '. Jonathan also works as a consultant, currently part-time as a Builder-In-Residence with PlaygroundAI.
