preface
If you’ve picked up this book, you’re probably aware of the extraordinary progress that deep learning has brought to the field of artificial intelligence. In just a handful of years, we went from near-unusable computer vision and natural language processing to highly performant systems deployed at scale in products you use every day. The consequences of this sudden progress extend to almost every industry. Deep learning is applied to a diverse range of important problems across domains as different as medical imaging, agriculture, autonomous driving, education, disaster prevention, and manufacturing. Digital assistants are becoming pervasive on nearly every consumer computing device.
Yet, we believe deep learning is still in its early days. AI is going to represent a much greater wave of disruption than anything that came before it. The technology will continue to make its way to every problem where it can provide some utility—a transformation that will take decades to play out. This is a transformation with profound societal implications, on a global scale, spanning industries and cultures. We strongly believe the only way to ensure such a transformation is beneficial for the people it will affect—all of us—is to radically democratize access to the underlying technology. We need to put understanding of deep learning—how it works, where it fails, and how to apply it—in the hands of as many people as possible, including people who aren’t researchers in the field.