![]() ![]() The increasing popularity of unrolled deep networks is due, in part, to their potential in developing efficient, high-performance (yet interpretable) network architectures from reasonably sized training sets. DNNs are currently widely used for many articial intelligence (AI) applications, including computer vision, speech recognition, and robotics. More recently, this direction has attracted enormous attention, and it is rapidly growing in both theoretic investigations and practical applications. This book provides a structured treatment of the key principles and techniques for enabling efcient process-ing of deep neural networks (DNNs). Unrolling methods were first proposed to develop fast neural network approximations for sparse coding. Integrated photonic convolutional neural networks are a way to speed up the calculations in convolutional neural networks using special optical devices. An emerging technique called algorithm unrolling, or unfolding, offers promise in eliminating these issues by providing a concrete and systematic connection between iterative algorithms that are widely used in signal processing and deep neural networks. INTRODUCTION Deep neural networks (DNNs) are currently the foundation for many modern articial intelligence (AI) applications 1. Photonics harnesses lights unique properties for transmitting and processing information, revolutionizing applications like data communication. Despite these gains, the future development and practical deployment of deep networks are hindered by their black-box nature, i.e., a lack of interpretability and the need for very large training sets. The original neural networks had very few layers in the network. Deep neural networks provide unprecedented performance gains in many real-world problems in signal and image processing. Within the domain of neural networks, we have an area called deep learning. ![]()
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