Download or read book Number Systems for Deep Neural Network Architectures written by Ghada Alsuhli and published by Springer Nature. This book was released on 2023-09-01 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides readers a comprehensive introduction to alternative number systems for more efficient representations of Deep Neural Network (DNN) data. Various number systems (conventional/unconventional) exploited for DNNs are discussed, including Floating Point (FP), Fixed Point (FXP), Logarithmic Number System (LNS), Residue Number System (RNS), Block Floating Point Number System (BFP), Dynamic Fixed-Point Number System (DFXP) and Posit Number System (PNS). The authors explore the impact of these number systems on the performance and hardware design of DNNs, highlighting the challenges associated with each number system and various solutions that are proposed for addressing them.

Download or read book Math and Architectures of Deep Learning written by Krishnendu Chaudhury and published by Simon and Schuster. This book was released on 2024-03-26 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt: Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. You'll peer inside the "black box" to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications. Math and Architectures of Deep Learning sets out the foundations of DL usefully and accessibly to working practitioners. Each chapter explores a new fundamental DL concept or architectural pattern, explaining the underpinning mathematics and demonstrating how they work in practice with well-annotated Python code. You'll start with a primer of basic algebra, calculus, and statistics, working your way up to state-of-the-art DL paradigms taken from the latest research. Learning mathematical foundations and neural network architecture can be challenging, but the payoff is big. You'll be free from blind reliance on pre-packaged DL models and able to build, customize, and re-architect for your specific needs. And when things go wrong, you'll be glad you can quickly identify and fix problems.

Download or read book Efficient Processing of Deep Neural Networks written by Vivienne Sze and published by Springer Nature. This book was released on 2022-05-31 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.

Download or read book Artificial Intelligence in Neuroscience Affective Analysis and Health Applications written by José Manuel Ferrández Vicente and published by Springer Nature. This book was released on 2022-05-24 with total page 675 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two volume set LNCS 13258 and 13259 constitutes the proceedings of the International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022, held in Puerto de la Cruz, Tenerife, Spain in May – June 2022. The total of 121 contributions was carefully reviewed and selected from 203 submissions. The papers are organized in two volumes, with the following topical sub-headings: Part I: Machine Learning in Neuroscience; Neuromotor and Cognitive Disorders; Affective Analysis; Health Applications, Part II: Affective Computing in Ambient Intelligence; Bioinspired Computing Approaches; Machine Learning in Computer Vision and Robot; Deep Learning; Artificial Intelligence Applications.

Download or read book Deep Learning Patterns and Practices written by Andrew Ferlitsch and published by Simon and Schuster. This book was released on 2021-10-12 with total page 755 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover best practices, reproducible architectures, and design patterns to help guide deep learning models from the lab into production. In Deep Learning Patterns and Practices you will learn: Internal functioning of modern convolutional neural networks Procedural reuse design pattern for CNN architectures Models for mobile and IoT devices Assembling large-scale model deployments Optimizing hyperparameter tuning Migrating a model to a production environment The big challenge of deep learning lies in taking cutting-edge technologies from R&D labs through to production. Deep Learning Patterns and Practices is here to help. This unique guide lays out the latest deep learning insights from author Andrew Ferlitsch’s work with Google Cloud AI. In it, you'll find deep learning models presented in a unique new way: as extendable design patterns you can easily plug-and-play into your software projects. Each valuable technique is presented in a way that's easy to understand and filled with accessible diagrams and code samples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Discover best practices, design patterns, and reproducible architectures that will guide your deep learning projects from the lab into production. This awesome book collects and illuminates the most relevant insights from a decade of real world deep learning experience. You’ll build your skills and confidence with each interesting example. About the book Deep Learning Patterns and Practices is a deep dive into building successful deep learning applications. You’ll save hours of trial-and-error by applying proven patterns and practices to your own projects. Tested code samples, real-world examples, and a brilliant narrative style make even complex concepts simple and engaging. Along the way, you’ll get tips for deploying, testing, and maintaining your projects. What's inside Modern convolutional neural networks Design pattern for CNN architectures Models for mobile and IoT devices Large-scale model deployments Examples for computer vision About the reader For machine learning engineers familiar with Python and deep learning. About the author Andrew Ferlitsch is an expert on computer vision, deep learning, and operationalizing ML in production at Google Cloud AI Developer Relations. Table of Contents PART 1 DEEP LEARNING FUNDAMENTALS 1 Designing modern machine learning 2 Deep neural networks 3 Convolutional and residual neural networks 4 Training fundamentals PART 2 BASIC DESIGN PATTERN 5 Procedural design pattern 6 Wide convolutional neural networks 7 Alternative connectivity patterns 8 Mobile convolutional neural networks 9 Autoencoders PART 3 WORKING WITH PIPELINES 10 Hyperparameter tuning 11 Transfer learning 12 Data distributions 13 Data pipeline 14 Training and deployment pipeline

Download or read book Handbook of Mobility Data Mining Volume 3 written by Haoran Zhang and published by Elsevier. This book was released on 2023-01-29 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Mobility Data Mining: Volume Three: Mobility Data-Driven Applications introduces the fundamental technologies of mobile big data mining (MDM), advanced AI methods, and upper-level applications, helping readers comprehensively understand MDM with a bottom-up approach. The book explains how to preprocess mobile big data, visualize urban mobility, simulate and predict human travel behavior, and assess urban mobility characteristics and their matching performance as conditions and constraints in transport, emergency management, and sustainability development systems. The book contains crucial information for researchers, engineers, operators, administrators, and policymakers seeking greater understanding of current technologies' infra-knowledge structure and limitations. The book introduces how to design MDM platforms that adapt to the evolving mobility environment—and new types of transportation and users—based on an integrated solution that utilizes sensing and communication capabilities to tackle significant challenges faced by the MDM field. This third volume looks at various cases studies to illustrate and explore the methods introduced in the first two volumes, covering topics such as Intelligent Transportation Management, Smart Emergency Management—detailing cases such as the Fukushima earthquake, Hurricane Katrina, and COVID-19—and Urban Sustainability Development, covering bicycle and railway travel behavior, mobility inequality, and road and light pollution inequality. - Introduces MDM applications from six major areas: intelligent transportation management, shared transportation systems, disaster management, pandemic response, low-carbon transportation, and social equality - Uses case studies to examine possible solutions that facilitate ethical, secure, and controlled emergency management based on mobile big data - Helps develop policy innovations beneficial to citizens, businesses, and society - Stems from the editor's strong network of global transport authorities and transport companies, providing a solid knowledge structure and data foundation as well as geographical and stakeholder coverage

Download or read book Deep Learning for Cognitive Computing Systems written by M.G. Sumithra and published by Walter de Gruyter GmbH & Co KG. This book was released on 2022-12-31 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cognitive computing simulates human thought processes with self-learning algorithms that utilize data mining, pattern recognition, and natural language processing. The integration of deep learning improves the performance of Cognitive computing systems in many applications, helping in utilizing heterogeneous data sets and generating meaningful insights.

Download or read book Intelligent Computing and Applications written by Subhransu Sekhar Dash and published by Springer Nature. This book was released on 2020-09-29 with total page 781 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the peer-reviewed proceedings of the 5th International Conference on Intelligent Computing and Applications (ICICA 2019), held in Ghaziabad, India, on December 6–8, 2019. The contributions reflect the latest research on advanced computational methodologies such as neural networks, fuzzy systems, evolutionary algorithms, hybrid intelligent systems, uncertain reasoning techniques, and other machine learning methods and their applications to decision-making and problem-solving in mobile and wireless communication networks.

Download or read book Deep Learning Concepts and Architectures written by Witold Pedrycz and published by Springer Nature. This book was released on 2019-10-29 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces readers to the fundamental concepts of deep learning and offers practical insights into how this learning paradigm supports automatic mechanisms of structural knowledge representation. It discusses a number of multilayer architectures giving rise to tangible and functionally meaningful pieces of knowledge, and shows how the structural developments have become essential to the successful delivery of competitive practical solutions to real-world problems. The book also demonstrates how the architectural developments, which arise in the setting of deep learning, support detailed learning and refinements to the system design. Featuring detailed descriptions of the current trends in the design and analysis of deep learning topologies, the book offers practical guidelines and presents competitive solutions to various areas of language modeling, graph representation, and forecasting.

Download or read book On Numerical Methods for Efficient Deep Neural Networks written by Chong Li and published by . This book was released on 2019 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt: The advent of deep neural networks has revolutionized a number of areas in machine learning, including image recognition, speech recognition, and natural language processing. Deep neural networks have demonstrated massive generalization power, with which domain-specific knowledge in certain machine learning tasks has become less crucial. However, the impressive generalization power of deep neural networks comes at the cost of highly complex models that are computationally expensive to evaluate and cumbersome to store in memory. The computation cost of training and evaluating neural networks is a major issue in practice. On edge devices such as cell phones and IoT devices, the hardware capability, as well as battery capacity, are quite limited. Deploying neural network applications on edge devices could easily lead to high latency and fast battery drainage. The storage size of a trained neural network is a concern on edge devices as well. Some state-of-the-art neural network models have hundreds of millions of parameters. Even storing such models on edge devices can be problematic. Although we can transfer the input to the neural network to a server and evaluate the neural network on the server-side, the computation cost of network evaluation directly relates to the financial cost of operating the server clusters. More importantly, many neural network applications, such as e-Commerce recommender systems, has stringent delay constraint. Overall speaking, the computation cost network evaluation directly impacts the bottom lines of companies deploying neural network applications. It is highly desirable to reduce the model size and computation cost of evaluating the neural network without degrading the performance of the network. The neural network uses a combination of simple linear operations (such as fully connected layer and convolutional layer) and non-linearities (such as ReLU function) to synthesis elaborated feature extractors. While such automatic feature engineering is among the major driving forces of the recent neural network renaissance, it also contributes to the high computation cost of neural networks. In other words, since we are synthesizing highly complex non-linear functions using very simple building blocks, it is inevitable that a large number of such simple building blocks have to be used for the network to be sufficiently expressive. What if we directly incorporate well-studied classical methods that are known to be helpful for feature extraction in the neural network? Such high-level operations could directly reflect the intent of the network designers so the network does not have to use a large number of simple building blocks. For the network to be end-to-end trainable, we will need to be able to compute the gradient of the operation that we incorporate into the network. The differentiability of the operation could be a limiting factor, since the gradient of operation may not exist, or difficult to compute. We shall demonstrate that incorporating carefully designed feature extractors in the neural network is indeed highly effective. Moreover, if the gradient is difficult to compute, an approximation of the gradient can be used in place of the true gradient without negatively impact the training of the neural network. In this dissertation, we explore applying well-studied numerical methods in the context of deep neural networks for computationally efficient network architectures. In Chapter 2, we present COBLA---Constrained Optimization Based Low-rank Approximation---a systematic method of finding an optimal low-rank approximation of a trained convolutional neural network, subject to constraints in the number of multiply-accumulate (MAC) operations and the memory footprint. COBLA optimally allocates the constrained computation resources into each layer of the approximated network. The singular value decomposition of the network weight is computed, then a binary masking variable is introduced to denote whether a particular singular value and the corresponding singular vectors are used in low-rank approximation. With this formulation, the number of the MAC operations and the memory footprint are represented as linear constraints in terms of the binary masking variables. The resulted 0-1 integer programming problem is approximately solved by sequential quadratic programming. COBLA does not introduce any hyperparameter. We empirically demonstrate that COBLA outperforms prior art using the SqueezeNet and VGG-16 architecture on the ImageNet dataset. Chapter 3 focuses on neural network based recommender systems, a vibrant research area with important industrial applications. Recommender systems on E-Commerce platforms track users' online behaviors and recommend relevant items according to each user's interests and needs. Bipartite graphs that capture both user/item features and user-item interactions have been demonstrated to be highly effective for this purpose. Recently, graph neural network (GNN) has been successfully applied in the representation of bipartite graphs in industrial recommender systems. Response time is a key consideration in the design and implementation of an industrial recommender system. Providing individualized recommendations on a dynamic platform with billions of users within tens of milliseconds is extremely challenging. In Chapter 2, we make a key observation that the users of an online E-Commerce platform can be naturally clustered into a set of communities. We propose to cluster the users into a set of communities and make recommendations based on the information of the users in the community collectively. More specifically, embeddings are assigned to the communities and the user information is decomposed into two parts, each of which captures the community-level generalizations and individualized preferences respectively. The community structure can be considered as an enhancement to the GNN methods that are inherently flat and do not learn hierarchical representations of graphs. The performance of the proposed algorithm is demonstrated on a public dataset and a world-leading E-Commerce company dataset. In Chapter 4, we propose a novel method to estimate the parameters of a collection of Hidden Markov Models (HMM), each of which corresponds to a set of known features. The observation sequence of an individual HMM is noisy and/or insufficient, making parameter estimation solely based on its corresponding observation sequence a challenging problem. The key idea is to combine the classical Expectation-Maximization (EM) algorithm with a neural network, while these two are jointly trained in an end-to-end fashion, mapping the HMM features to its parameters and effectively fusing the information across different HMMs. In order to address the numerical difficulty in computing the gradient of the EM iteration, simultaneous perturbation stochastic approximation (SPSA) is employed to estimate the gradient. We also provide a rigorous proof that the estimated gradient due to SPSA converges to the true gradient almost surely. The efficacy of the proposed method is demonstrated on synthetic data as well as a real-world e-Commerce dataset.

Download or read book Research Anthology on Decision Support Systems and Decision Management in Healthcare Business and Engineering written by Management Association, Information Resources and published by IGI Global. This book was released on 2021-05-28 with total page 1538 pages. Available in PDF, EPUB and Kindle. Book excerpt: Decision support systems (DSS) are widely touted for their effectiveness in aiding decision making, particularly across a wide and diverse range of industries including healthcare, business, and engineering applications. The concepts, principles, and theories of enhanced decision making are essential points of research as well as the exact methods, tools, and technologies being implemented in these industries. From both a standpoint of DSS interfaces, namely the design and development of these technologies, along with the implementations, including experiences and utilization of these tools, one can get a better sense of how exactly DSS has changed the face of decision making and management in multi-industry applications. Furthermore, the evaluation of the impact of these technologies is essential in moving forward in the future. The Research Anthology on Decision Support Systems and Decision Management in Healthcare, Business, and Engineering explores how decision support systems have been developed and implemented across diverse industries through perspectives on the technology, the utilizations of these tools, and from a decision management standpoint. The chapters will cover not only the interfaces, implementations, and functionality of these tools, but also the overall impacts they have had on the specific industries mentioned. This book also evaluates the effectiveness along with benefits and challenges of using DSS as well as the outlook for the future. This book is ideal for decision makers, IT consultants and specialists, software developers, design professionals, academicians, policymakers, researchers, professionals, and students interested in how DSS is being used in different industries.

Download or read book Convergence of Deep Learning in Cyber IoT Systems and Security written by Rajdeep Chakraborty and published by John Wiley & Sons. This book was released on 2022-11-08 with total page 485 pages. Available in PDF, EPUB and Kindle. Book excerpt: CONVERGENCE OF DEEP LEARNING IN CYBER-IOT SYSTEMS AND SECURITY In-depth analysis of Deep Learning-based cyber-IoT systems and security which will be the industry leader for the next ten years. The main goal of this book is to bring to the fore unconventional cryptographic methods to provide cyber security, including cyber-physical system security and IoT security through deep learning techniques and analytics with the study of all these systems. This book provides innovative solutions and implementation of deep learning-based models in cyber-IoT systems, as well as the exposed security issues in these systems. The 20 chapters are organized into four parts. Part I gives the various approaches that have evolved from machine learning to deep learning. Part II presents many innovative solutions, algorithms, models, and implementations based on deep learning. Part III covers security and safety aspects with deep learning. Part IV details cyber-physical systems as well as a discussion on the security and threats in cyber-physical systems with probable solutions. Audience Researchers and industry engineers in computer science, information technology, electronics and communication, cybersecurity and cryptography.

Download or read book Dynamic Neural Networks for Robot Systems Data Driven and Model Based Applications written by Long Jin and published by Frontiers Media SA. This book was released on 2024-07-24 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural network control has been a research hotspot in academic fields due to the strong ability of computation. One of its wildly applied fields is robotics. In recent years, plenty of researchers have devised different types of dynamic neural network (DNN) to address complex control issues in robotics fields in reality. Redundant manipulators are no doubt indispensable devices in industrial production. There are various works on the redundancy resolution of redundant manipulators in performing a given task with the manipulator model information known. However, it becomes knotty for researchers to precisely control redundant manipulators with unknown model to complete a cyclic-motion generation CMG task, to some extent. It is worthwhile to investigate the data-driven scheme and the corresponding novel dynamic neural network (DNN), which exploits learning and control simultaneously. Therefore, it is of great significance to further research the special control features and solve challenging issues to improve control performance from several perspectives, such as accuracy, robustness, and solving speed.

Download or read book Neural Network Design written by Martin T. Hagan and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Emerging Electronic Devices Circuits and Systems written by Chandan Giri and published by Springer Nature. This book was released on 2023-06-01 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book constitutes peer-reviewed proceedings of a workshop on Emerging Electronics Devices, Circuits, and Systems (EEDCS) held in conjunction with International Symposium on Devices, Circuits, and Systems (ISDCS 2022). The book focuses on the recent development in devices, circuits, and systems. It also discusses innovations, trends, practical challenges, and solutions adopted in device design, modeling, fabrication, characterization, and their circuit implementation with pertinent system applications. It will be useful for researchers, developers, engineers, academicians, and students.

Download or read book Intelligent Systems Modeling and Simulation II written by Samsul Ariffin Abdul Karim and published by Springer Nature. This book was released on 2022-10-12 with total page 688 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book develops a new system of modeling and simulations based on intelligence system. As we are directly moving from Third Industrial Revolution (IR3.0) to Fourth Industrial Revolution (IR4.0), there are many emergence techniques and algorithm that appear in many sciences and engineering branches. Nowadays, most industries are using IR4.0 in their product development as well as to refine their products. These include simulation on oil rig drilling, big data analytics on consumer analytics, fastest algorithm for large-scale numerical simulations and many more. These will save millions of dollar in the operating costs. Without any doubt, mathematics, statistics and computing are well blended to form an intelligent system for simulation and modeling. Motivated by this rapid development, in this book, a total of 41 chapters are contributed by the respective experts. The main scope of the book is to develop a new system of modeling and simulations based on machine learning, neural networks, efficient numerical algorithm and statistical methods. This book is highly suitable for postgraduate students, researchers as well as scientists that have interest in intelligent numerical modeling and simulations.

Download or read book Intelligent Systems written by Ricardo Cerri and published by Springer Nature. This book was released on 2020-10-15 with total page 666 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNAI 12319 and 12320 constitutes the proceedings of the 9th Brazilian Conference on Intelligent Systems, BRACIS 2020, held in Rio Grande, Brazil, in October 2020. The total of 90 papers presented in these two volumes was carefully reviewed and selected from 228 submissions. The contributions are organized in the following topical section: Part I: Evolutionary computation, metaheuristics, constrains and search, combinatorial and numerical optimization; neural networks, deep learning and computer vision; and text mining and natural language processing. Part II: Agent and multi-agent systems, planning and reinforcement learning; knowledge representation, logic and fuzzy systems; machine learning and data mining; and multidisciplinary artificial and computational intelligence and applications. Due to the Corona pandemic BRACIS 2020 was held as a virtual event.