The Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies. prepared them for careers in deep learning, machine learning. artificial intelligence in top companies in industry and academia. The book begins by covering the foundations of deep learning, followed by key deep learning architectures. Subsequent parts on generative models and reinforcement learning may be used as part of a deep learning course or as part of a course on each topic. The book includes state of the art topics such as Transformers, graph neural networks, variational autoencoders. deep reinforcement learning, with a broad range of applications. The appendices provide equations for computing gradients in backpropagation and optimization. best practices in scientific writing and reviewing. The text presents an up to date guide to the field built upon clear visualizations using a unified notation and equations, lowering the barrier to entry for the reader. The accompanying website provides complementary code and hundreds of exercises with solutions.