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Welcome to the MEAP for Learn Generative AI with PyTorch. This book is designed for those who have a good grasp of Python and a basic understanding of machine learning, particularly neural networks. It aims to guide you through the creation of generative models from the ground up.

My fascination with generative AI began a few years back, when I first saw models converting horse images into zebra images and Transformers producing lifelike text. This book is born out of my journey in building and understanding these models from scratch. It's the book I wish I had during my experiments with various generative models. It begins with simple models, helping readers build foundational deep learning skills before advancing to more complex challenges. I chose PyTorch for its dynamic computational graph and clear syntax after experimenting with TensorFlow.

All generative models in this book are deep neural networks. The book starts with a comprehensive deep learning project in PyTorch, ideal for those new to the field. Each chapter is carefully structured to build upon the previous one, especially beneficial for readers new to deep learning in PyTorch. You'll start by creating basic content like shapes, numbers, and images using Generative Adversarial Networks (GANs) with straightforward architectures. As you progress, the complexity increases, culminating in building advanced models like Transformers.