To get the most out of this book

Readers should have a foundational understanding of Python programming and a basic grasp of machine learning concepts. Familiarity with deep learning frameworks such as TensorFlow or PyTorch will be beneficial but not essential. The book assumes an intermediate level of Python proficiency, enabling readers to focus on the generative AI concepts and applications covered throughout the chapters.

Software/hardware covered in the book	Operating system requirements
Python 3	GPU-enabled Windows, macOS, or Linux

The book’s coding examples are designed to be compatible with Python 3 and run on Windows, macOS, or Linux operating systems. To fully engage with the hands-on tutorials and examples, access to a GPU is recommended, as many generative AI models are computationally intensive. The book provides guidance on setting up a suitable development environment, including instructions for installing necessary libraries and dependencies.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

Throughout the book, readers are encouraged to actively experiment with the code samples provided and adapt them to their own projects. The companion GitHub repository serves as a valuable resource, offering more complete and modular versions of the code examples presented in the chapters. Accessing and working with this code will enhance the reader’s learning experience and help solidify their understanding of the concepts covered.