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Learn how to use generative media techniques with AI to create novel images or music in this practical, hands-on guide. Data scientists and software engineers will understand how state-of-the-art generative models work, how to fine-tune and adapt them to your needs, and how to combine existing building blocks to create new models and creative applications in different domains.
This book introduces theoretical concepts in an intuitive way, with extensive code samples and illustrations that you can run on services such as Google Colaboratory, Kaggle, or Hugging Face Spaces with minimal setup. You'll learn how to use open source libraries such as Transformers and Diffusers, conduct code exploration, and study several existing projects to help guide your work.
This book isn’t just for experts – it’s for anyone who wants to learn about this fascinating new field. We won’t focus on building models from scratch or diving straight into complicated mathematics. Instead, we’ll leverage existing models to solve real-world problems, helping you to build a solid intuition around how these techniques work and providing the foundation for you to keep exploring.
This hands-on approach, we hope, will help you get up and running quickly and efficiently with generative AI. You’ll learn how to use pre-trained models, adapt them for your needs, and generate new data with them. You’ll also learn how to evaluate the quality of generated data and explore ethical and social issues that may arise from using generative AI. This exposure will allow you to stay up to date with new models and help you identify areas that you may want to explore more deeply.
Learn the fundamentals of classic and modern generative AI techniques
Build and customize models that can generate text, images, and sound
Explore trade-offs between training from scratch and using large, pretrained models
Create models that can modify images by transferring the style of other images
Tweak and bend transformers and diffusion models for creative purposes
Train a model that can write text based on your style
Deploy models as interactive demos or services
Prerequisites:
This book assumes that you are comfortable programming in Python and have a foundational understanding of what Machine Learning is, including basic usage of frameworks like PyTorch or TensorFlow. Having practical experience with training models is not required, but it will be helpful to understand the content with more depth