\u003cp\u003e\u003ci\u003eDeep Learning through Sparse Representation and Low-Rank Modeling\u003c/i\u003e bridges classical sparse and low rank modelsâthose that emphasize problem-specific Interpretabilityâwith recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining.\u003c/p\u003e \u003cp\u003eThis book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics.\u003c/p\u003e\u003cul\u003e \u003cli\u003eCombines classical sparse and low-rank models and algorithms with the latest advances in deep learning networks\u003c/li\u003e \u003cli\u003eShows how the structure and algorithms of sparse and low-rank methods improves the performance and interpretability of Deep Learning models\u003c/li\u003e \u003cli\u003eProvides tactics on how to build and apply customized deep learning models for various applications\u003c/li\u003e\u003c/ul\u003e
nl
en