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Generative AI is one of the most revolutionary technologies of our time, transforming the way we interact with machines. Its potential to revolutionize the way we live, work, and play has been the subject of countless conversations, debates, and predictions. But what if there was an even greater potential to this powerful technology? What if the possibilities of generative AI extend beyond our current imagination? The future of generative AI may be more exciting than we ever thought possible… Since our earliest days, we have sought opportunities to generate original and beautiful creations. For early humans, this took the form of cave paintings depicting wild animals and abstract patterns, created with pigments placed carefully and methodically onto rock. The Romantic Era gave us the mastery of Tchaikovsky symphonies, with their ability to inspire feelings of triumph and tragedy through sound waves, woven together to form beautiful melodies and harmonies. And in recent times, we have found ourselves rushing to bookshops at midnight to buy stories about a fictional wizard, because the combination of letters creates a narrative that wills us to turn the page and find out what happens to our hero. It is therefore not surprising that humanity has started to ask the ultimate question of creativity: can we create something that is in itself creative? This is the question that generative AI aims to answer. With recent advances in methodology and technology, we are now able to build machines that can paint original artwork in a given style, write coherent blocks of text with long-term structure, compose music that is pleasant to listen to, and develop winning strategies for complex games by generating imaginary future scenarios. This is just the start of a generative revolution that will leave us with no choice but to find answers to some of the biggest questions about the mechanics of creativity, and ultimately, what it means to be human. In short, there has never been a better time to learn about generative AI—so let’s get started! This book assumes no prior knowledge of generative AI. We will build up all of the key concepts from scratch in a way that is intuitive and easy to follow, so don’t worry if you have no experience with generative AI. You have come to the right place! Rather than only covering the techniques that are currently in vogue, this book serves as a complete guide to generative modeling that covers a broad range of model families. There is no one technique that is objectively better or worse than any other—in fact, many state-of-the-art models now mix together ideas from across the broad spectrum of approaches to generative modeling. For this reason, it is important to keep abreast of developments across all areas of generative AI, rather than focusing on one particular kind of technique. One thing is certain: the field of generative AI is moving fast, and you never know where the next groundbreaking idea will come from! With this in mind, the approach I will take is to show you how to train your own generative models on your own data, rather than relying on pre-trained off-the-shelf models. While there are now many impressive open source generative models that can be downloaded and run in a few lines of code, the aim of this book is to dig deeper into their architecture and design from first principles, so that you gain a complete understanding of how they work and can code up examples of each technique from scratch using Python and Keras. In summary, this book can be thought of as a map of the current generative AI landscape that covers both theory and practical applications, including full working examples of key models from the literature. We will walk through the code for each step by step, with clear signposts that show how the code implements the theory underpinning each technique. This book can be read cover to cover or used as a reference book that you can dip into. Above all, I hope you find it a useful and enjoyable read!