Number Systems for Deep Neural Network Architectures PDF Download

Author: Ghada Alsuhli
Publisher: Springer Nature
ISBN: 3031381335
Category : Technology & Engineering
Languages : en
Pages : 100

View

Book Description
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.

Author: Krishnendu Chaudhury
Publisher: Simon and Schuster
ISBN: 1617296481
Category : Computers
Languages : en
Pages : 550

View

Book Description
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.

Author: Vivienne Sze
Publisher: Springer Nature
ISBN: 3031017668
Category : Technology & Engineering
Languages : en
Pages : 254

View

Book Description
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.

Author: José Manuel Ferrández Vicente
Publisher: Springer Nature
ISBN: 3031062426
Category : Medical
Languages : en
Pages : 675

View

Book Description
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.

Author: Andrew Ferlitsch
Publisher: Simon and Schuster
ISBN: 163835667X
Category : Computers
Languages : en
Pages : 755

View

Book Description
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

Author: Haoran Zhang
Publisher: Elsevier
ISBN: 0443184232
Category : Business & Economics
Languages : en
Pages : 244

View

Book Description
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

Author: Ian Goodfellow
Publisher: MIT Press
ISBN: 0262337371
Category : Computers
Languages : en
Pages : 801

View

Book Description
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Author: M.G. Sumithra
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110750589
Category : Computers
Languages : en
Pages : 214

View

Book Description
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.

Author: Chong Li
Publisher:
ISBN:
Category :
Languages : en
Pages : 80

View

Book Description
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.

Author: Subhransu Sekhar Dash
Publisher: Springer Nature
ISBN: 9811555664
Category : Technology & Engineering
Languages : en
Pages : 781

View

Book Description
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.