Computer Engineering Machine Learning and Neural Networks
Description
This is the first textbook focusing on practicality of machine learning (ML) and deep neural networks (DNN), by introducing methods that enable engineering applications of ML and DNN models. The authors describe many methodologies that are widely used in designing, training, and deploying of these models and discuss their applicability under various contexts. Coverage begins with the basic knowledge of machine learning and deep neural networks and their applications in solving practical engineering problems. It then proceeds through a series of computer engineering methods commonly used in developing machine learning and deep neural network models. The book also explains how to improve the training and inference performance in terms of model accuracy, size, runtime, etc. by considering various requirements and availability of data in the applications. Techniques that are widely adopted in both industry and academia are discussed. Tutorials and projects designed to practice the introduced techniques are provided using popular development frameworks of machine learning.
-
Emphasizes practice over theoretical foundations, making content accessible to engineering students and engineers;
-
Includes in-depth discussion of popular DNN models and their applications;
-
Discusses engineering methods and tricks widely adopted in practice for using ML and DNN to solve engineering problems.
More details
Other editions
Additional editions
Persons
Yiran Chen is the John Cocke Distinguished Professor of Electrical and Computer Engineering at Duke University. He serves as the Principal Investigator and Director of the NSF AI Institute for Edge Computing Leveraging Next Generation Networks (Athena) and Co-Director of the Duke Center for Computational Evolutionary Intelligence (DCEI). His research group focuses on innovations in emerging memory and storage systems, machine learning and neuromorphic computing, and edge computing. Dr. Chen has authored over 700 publications and holds 96 U.S. patents. His work has received widespread recognition, including two Test-of-Time Awards and 14 Best Paper/Poster Awards. He is the recipient of the IEEE Circuits and Systems Society's Charles A. Desoer Technical Achievement Award and the IEEE Computer Society's Edward J. McCluskey Technical Achievement Award. He also serves as the inaugural Editor-in-Chief of the IEEE Transactions on Circuits and Systems for Artificial Intelligence (TCASAI) and the founding Chair of the IEEE Circuits and Systems Society's Machine Learning Circuits and Systems (MLCAS) Technical Committee. Dr. Chen is a Fellow of the AAAS, ACM, IEEE, and NAI, and a member of the European Academy of Sciences and Arts.
Hai (Helen) Li is the Marie Foote Reel E'46 Distinguished Professor and Department Chair of the Electrical and Computer Engineering Department at Duke University. She received her B.S. and M.S. from Tsinghua University and her Ph.D. from Purdue University. Her research interests include neuromorphic circuits and systems for brain-inspired computing, machine learning acceleration and trustworthy AI, conventional and emerging memory design and architecture, and software and hardware co-design. Dr. Li served/serves as the Associate Editor-in-Chief and Associate Editor for multiple IEEE and ACM journals. She was the General Chair or Technical Program Chair of numerous IEEE/ACM conferences and the Technical Program Committee member of over 30 international conference series. Dr. Li is a Distinguished Lecturer of the IEEE CAS Society and a Distinguished Speaker of ACM. Dr. Li is a recipient of the IEEE Edward J. McCluskey Technical Achievement Award, Ten Year Retrospective Influential Paper Award from ICCAD, TUM-IAS Hans Fischer Fellowship from Germany, ELATE Fellowship, nine best paper awards, and another ten best paper nominations. Dr. Li is a fellow of ACM, AAAS, IEEE, and NAI.
Content
Introduction.- Perceptron and back propagation.- Image feature and 2d convolution.- Convolutional neural network.- Efficient neural architecture.- Recurrent neural network and language models.- Transfer learning.- Generative adversarial network gan.- Automl and neural architecture search.- Distributed computing and federated learning.- Summary.
