Deep Learning on Embedded Systems

Deep Learning on Embedded Systems: A Hands-On Approach Using Jetson Nano and Raspberry Pi focuses on the implementation of deep learning for engineering tasks. Deep learning is a rapidly growing branch of artificial intelligence that has primarily belonged to mathematicians, computer scientists, and data scientists. However, as the field continues to expand, its reach has grown to include scientists and engineers of many different backgrounds. Within engineering in particular, deep learning already has a wide variety of applications, such as autonomous cars, intelligent robotics, computer vision, natural language processing, and bioinformatics. With this trend in mind, the book aims to provide the basic, practical knowledge necessary for engineering students and educators to expand and solidify their knowledge of deep learning. It features practical implementation cases involving computers and widely available embedded hardware and software systems such as Raspberry Pi and Nvidia Jeston Nano. This will allow for readers to learn to apply their knowledge to their own particular field and incorporate deep learning models into design and research projects. Readers will come away with a fundamental understanding of deep learning, computer vision, natural language processing, and deep learning frameworks, along with the skillset needed for image classification, image captioning, transfer learning on emebdded systems, and PyTorch and Cuda installations.