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lgli/深度学习基础.pdf

深度学习基础 it-ebooks iBooker it-ebooks, it-ebooks-extra

This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms into deep learning algorithms. The book’s third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning. Erscheinungsdatum: 26.07.2023

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lgli/机器学习和深度学习的新式金融应用.pdf

机器学习和深度学习的新式金融应用 it-ebooks iBooker it-ebooks, it-ebooks-extra

This book presents the state-of-the-art applications of machine learning in the finance domain with a focus on financial product modeling, which aims to advance the model performance and minimize risk and uncertainty. It provides both practical and managerial implications of financial and managerial decision support systems which capture a broad range of financial data traits. It also serves as a guide for the implementation of risk-adjusted financial product pricing systems, while adding a significant supplement to the financial literacy of the investigated study. The book covers advanced machine learning techniques, such as Support Vector Machine, Neural Networks, Random Forest, K -Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches, and their application to finance datasets. It also leverages real-world financial instances to practice business product modeling and data analysis. Software code, such as MATLAB, Python and/or R including datasets within a broad range of financial domain are included for more rigorous practice. The book primarily aims at providing graduate students and researchers with a roadmap for financial data analysis. It is also intended for a broad audience, including academics, professional financial analysts, and policy-makers who are involved in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.

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lgli/使用机器学习和深度学习的防疫系统设计.pdf

使用机器学习和深度学习的防疫系统设计 it-ebooks iBooker it-ebooks, it-ebooks-extra

This book explores the benefits of deploying Machine Learning (ML) and Artificial Intelligence (AI) in the health care environment. The authors study different research directions that are working to serve challenges faced in building strong healthcare infrastructure with respect to the pandemic crisis. The authors take note of obstacles faced in the rush to develop and alter technologies during the Covid crisis. They study what can be learned from them and what can be leveraged efficiently. The authors aim to show how healthcare providers can use technology to exploit advances in machine learning and deep learning in their own applications. Topics include remote patient monitoring, data analysis of human behavioral patterns, and machine learning for decision making in real-time. Erscheinungsdatum: 02.02.2023

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lgli/应用深度学习.pdf

应用深度学习 it-ebooks iBooker it-ebooks, it-ebooks-extra

"This book focuses on the applied aspects of artificial intelligence using enterprise frameworks and technologies. The book is applied in nature and will equip the reader with the necessary skills and understanding for delivering enterprise ML technologies. It will be valuable for undergraduate and postgraduate students in subjects such as artificial intelligence and data science, and also for industrial practitioners engaged with data analytics and machine learning tasks. The book covers all of the key conceptual aspects of the field and provides a foundation for all interested parties to develop their own artificial intelligence applications"--Back cover

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lgli/Jess H. Lonner - 膝关节和髋关节置换术中的机器人技术 (2019, Springer).epub

膝关节和髋关节置换术中的机器人技术 Jess H. Lonner Springer International Publishing : Imprint: Springer, Springer Nature, Cham, 2019

This state-of-the-art book focuses specifically on the current and emerging uses of robotics for knee and hip arthroplasty, with an expanding market anticipated, particularly as costs drop, data emerges and surgical efficiencies improve. It is divided into four main sections. Part one covers the background and basic principles of robotics in orthopedic surgery, discussing its history and evolution, current concepts and available technologies, perioperative protocols for recovery and pain management, economic considerations, and risks and complications. The second and third parts focus on the techniques themselves for the knee and hip respectively, including unicompartmental and bicompartmental knee arthroplasty, patellofemoral arthroplasty, and total knee and hip arthroplasty utilizing Navio, Mako, iThink, Omni and ROSA Knee robots. The final section presents the emerging use of robotics in spine surgery as well as for hospital process improvement. Presenting the most current techniques, technology and evidence, Robotics in Knee and Hip Arthroplasty will be a valuable resource for orthopedic surgeons, residents and fellows looking to implement and utilize these developing management strategies in their clinical practice.

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lgli/Jürgen Georg Backhaus、Günther Chaloupek、Hans A. Frambach - 弗里德里希·恩格斯诞辰 200 周年: 对其一生和学术的批判性评估【自排文本】.pdf

弗里德里希·恩格斯诞辰 200 周年: 对其一生和学术的批判性评估【自排文本】 Jürgen Georg Backhaus; Günther Chaloupek; Hans A. Frambach Springer International Publishing Springer, Springer Nature, Cham, 2022

谷歌机译。本书讨论了弗里德里希·恩格斯的生平和学术。这些稿件是为了纪念恩格斯诞辰 200 周年而写的,从不同的角度审视了他的研究,追溯了他的前辈的影响,并批判性地评价了他在 19 世纪学术界的地位。此外,还讨论了具体的话题,例如他对美国资本主义的(错误)评估、他对意大利劳工运动的影响、社会问题的主题化以及他的思想在全球经济中的相关性。本书以全新的视角审视了科学社会主义的共同创始人,当代政治、社会和经济体系、经济思想史和政治史的研究人员和学生将会对此感兴趣。

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lgli/多元化深度学习.pdf

多元化深度学习 it-ebooks iBooker it-ebooks, it-ebooks-extra

This book covers a large set of methods in the field of Artificial Intelligence - Deep Learning applied to real-world problems. The fundamentals of the Deep Learning approach and different types of Deep Neural Networks (DNNs) are first summarized in this book, which offers a comprehensive preamble for further problem–oriented chapters. The most interesting and open problems of machine learning in the framework of Deep Learning are discussed in this book and solutions are proposed. This book illustrates how to implement the zero-shot learning with Deep Neural Network Classifiers, which require a large amount of training data. The lack of annotated training data naturally pushes the researchers to implement low supervision algorithms. Metric learning is a long-term research but in the framework of Deep Learning approaches, it gets freshness and originality. Fine-grained classification with a low inter-class variability is a difficult problem for any classification tasks. This book presents how it is solved, by using different modalities and attention mechanisms in 3D convolutional networks. Researchers focused on Machine Learning, Deep learning, Multimedia and Computer Vision will want to buy this book. Advanced level students studying computer science within these topic areas will also find this book useful.

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lgli/大数据和深度学习的近期进展.pdf

大数据和深度学习的近期进展 it-ebooks iBooker it-ebooks, it-ebooks-extra

"This book presents the original articles that have been accepted in the 2019 INNS Big Data and Deep Learning (INNS BDDL) international conference, a major event for researchers in the field of artificial neural networks, big data and related topics, organized by the International Neural Network Society and hosted by the University of Genoa. In 2019 INNS BDDL has been held in Sestri Levante (Italy) from April 16 to April 18. More than 80 researchers from 20 countries participated in the INNS BDDL in April 2019. In addition to regular sessions, INNS BDDL welcomed around 40 oral communications, 6 tutorials have been presented together with 4 invited plenary speakers. This book covers a broad range of topics in big data and deep learning, from theoretical aspects to state-of-the-art applications. This book is directed to both Ph.D. students and Researchers in the field in order to provide a general picture of the state-of-the-art on the topics addressed by the conference. ." - Prové de l'editor

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lgli/深度学习概念和架构.pdf

深度学习概念和架构 it-ebooks iBooker it-ebooks, it-ebooks-extra

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. Erscheinungsdatum: 13.11.2019

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upload/duxiu_main2/【星空藏书馆】/【星空藏书馆】等多个文件/Kindle电子书库（012）/2022更新/2022/7月/Processing-in-Memory for AI From Circuits to Systems (Joo-Young Kim, Bongjin Kim, Tony Tae-Hyoung Kim) (z-lib.org).pdf

Processing-in-Memory for AI : From Circuits to Systems Joo-Young Kim, Bongjin Kim, Tony Tae-Hyoung Kim Springer International Publishing AG, Springer Nature, Cham, 2022

This book provides a comprehensive introduction to processing-in-memory (PIM) technology, from its architectures to circuits implementations on multiple memory types and describes how it can be a viable computer architecture in the era of AI and big data. The authors summarize the challenges of AI hardware systems, processing-in-memory (PIM) constraints and approaches to derive system-level requirements for a practical and feasible PIM solution. The presentation focuses on feasible PIM solutions that can be implemented and used in real systems, including architectures, circuits, and implementation cases for each major memory type (SRAM, DRAM, and ReRAM). Erscheinungsdatum: 10.07.2022

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lgli/计算机视觉和深度学习中的领域自适应.pdf

计算机视觉和深度学习中的领域自适应 it-ebooks iBooker it-ebooks, it-ebooks-extra

This book provides a survey of deep learning approaches to domain adaptation in computer vision. It gives the reader an overview of the state-of-the-art research in deep learning based domain adaptation. This book also discusses the various approaches to deep learning based domain adaptation in recent years. It outlines the importance of domain adaptation for the advancement of computer vision, consolidates the research in the area and provides the reader with promising directions for future research in domain adaptation. Divided into four parts, the first part of this book begins with an introduction to domain adaptation, which outlines the problem statement, the role of domain adaptation and the motivation for research in this area. It includes a chapter outlining pre-deep learning era domain adaptation techniques. The second part of this book highlights feature alignment based approaches to domain adaptation. The third part of this book outlines image alignment procedures for domain adaptation. The final section of this book presents novel directions for research in domain adaptation. This book targets researchers working in artificial intelligence, machine learning, deep learning and computer vision. Industry professionals and entrepreneurs seeking to adopt deep learning into their applications will also be interested in this book.

英语 [en] · 中文 [zh] · PDF · 8.1MB · 2020 · 📘 非小说类图书 · 🚀/lgli/lgrs · Save

upload/duxiu_main2/【星空藏书馆】/【星空藏书馆】等多个文件/Kindle电子书库（012）/2022更新/2022/7月/Cracked it (Bernard Garrette, Corey Phelps, Olivier Sibony) (rg).pdf

Cracked it! : how to solve big problems and sell solutions like top strategy consultants Bernard Garrette, Corey Phelps, Olivier Sibony Springer International Publishing Imprint : Palgrave Macmillan, 1st ed. 2018, PS, 2018

Solving complex problems and selling their solutions is critical for personal and organizational success. For most of us, however, it doesn’t come naturally and we haven’t been taught how to do it well. Research shows a host of pitfalls trips us up when we try: We’re quick to believe we understand a situation and jump to a flawed solution. We seek to confirm our hypotheses and ignore conflicting evidence. We view challenges incompletely through the frameworks we know instead of with a fresh pair of eyes. And when we communicate our recommendations, we forget our reasoning isn’t obvious to our audience. How can we do it better? In Cracked It! , seasoned strategy professors and consultants Bernard Garrette, Corey Phelps and Olivier Sibony present a rigorous and practical four-step approach to overcome these pitfalls. Building on tried-and-tested (but rarely revealed) methods of top strategy consultants, research in cognitive psychology, and the latest advances in design thinking, they provide a step-by-step process and toolkit that will help readers tackle any challenging business problem. Using compelling stories and detailed case examples, the authors guide readers through each step in the process: from how to state, structure and then solve problems to how to sell the solutions. Written in an engaging style by a trio of experts with decades of experience researching, teaching and consulting on complex business problems, this book will be an indispensable manual for anyone interested in creating value by helping their organizations crack the problems that matter most.

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lgli/Giuseppe Carbone - 医疗和服务机器人的新趋势：理论和实践的进步 (2019, Springer).epub

医疗和服务机器人的新趋势:理论和实践的进步 Giuseppe Carbone; Marco Ceccarelli; Doina Pisla; Springer International Publishing Springer International Publishing Springer, Mechanisms and Machine Science, 1st edition 2019, Cham, 2018

This book contains the selected papers of the Sixth International Workshop on Medical and Service Robots (MESROB 2018), held in Cassino, Italy, in 2018. The main topics of the workshop include: design of medical devices, kinematics and dynamics for medical robotics, exoskeletons and prostheses, anthropomorphic hands, therapeutic robots and rehabilitation, cognitive robots, humanoid and service robots, assistive robots and elderly assistance, surgical robots, human-robot interfaces, haptic devices, and medical treatments.

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lgli/深度学习基础指南.pdf

深度学习基础指南 it-ebooks iBooker it-ebooks, it-ebooks-extra

This stimulating text/reference presents a philosophical exploration of the conceptual foundations of deep learning, presenting enlightening perspectives that encompass such diverse disciplines as computer science, mathematics, logic, psychology, and cognitive science. The text also highlights select topics from the fascinating history of this exciting field, including the pioneering work of Rudolf Carnap, Warren McCulloch, Walter Pitts, Bulcsú László, and Geoffrey Hinton. Topics and features: Provides a brief history of mathematical logic, and discusses the critical role of philosophy, psychology, and neuroscience in the history of AI Presents a philosophical case for the use of fuzzy logic approaches in AI Investigates the similarities and differences between the Word2vec word embedding algorithm, and the ideas of Wittgenstein and Firth on linguistics Examines how developments in machine learning provide insights into the philosophical challenge of justifying inductive inferences Debates, with reference to philosophical anthropology, whether an advanced general artificial intelligence might be considered as a living being Investigates the issue of computational complexity through deep-learning strategies for understanding AI-complete problems and developing strong AI Explores philosophical questions at the intersection of AI and transhumanism This inspirational volume will rekindle a passion for deep learning in those already experienced in coding and studying this discipline, and provide a philosophical big-picture perspective for those new to the field.

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lgli/wiki - MGTOW中文维基百科——未删改前.pdf

MGTOW中文维基百科——未删改前 wiki Harvard University, Department of Sanskrit & Indian Studies, Harvard University Press, Cambridge, Massachusetts, 2018

This book explores the discrimination encountered and propagated by individuals in online environments. The editors develop the concept of 'online othering' as a tool through which to analyse and make sense of the myriad toxic and harmful behaviours which are being created through, or perpetuated via, the use of communication-technologies such as the internet, social media, and 'the internet of things'. The book problematises the dichotomy assumed between real and virtual spaces by exploring the construction of online abuse, victims' experiences, resistance to online othering, and the policing of interpersonal cyber-crime. The relationship between various socio-political institutions and experiences of online hate speech are also explored. Online Othering explores the extent to which forms of information-technologies facilitate, exacerbate, and/or promote the enactment of traditional offline offences (such as domestic abuse and stalking). It focuses on the construction and perpetration of online abuse through examples such as the far-right, the alt-right and Men's Rights Activists. It also explores experiences of, and resistance to, online abuse via examples such as victims' experiences of revenge porn, online abuse and misogyny, transphobia, disability hate crime, and the ways in which online othering is intersectional. Finally, the collection addresses the role of the police and other agencies in terms of their interventions, and the regulation and governance of virtual space(s). Contributions to the volume come from fields including sociology; communication and media studies; psychology; criminology; political studies; information science and gender studies. Online Othering is one of the very first collections to explore a multitude of abuses and their relationship to information and communication technology. -- Provided by publisher

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lgli/Jaeyoun Kim - 微型软机器人：动机，进步和展望 (2017, Springer).epub

微型软机器人:动机,进步和展望 Jaeyoun (Jay) Kim Springer International Publishing Imprint : Springer, SpringerBriefs in Applied Sciences and Technology, 2016

This book presents the technological basics and applications of small-scale (mm to sub-mm in length-scales) soft robots and devices, written for researchers in both academia and industry. Author Jaeyoun Kim presents technological motivations, enabling factors, and examples in an inter-linked fashion, making it easy for readers to understand and explore how microscale soft robots are a solution to researchers in search of technological platforms for safe, human-friendly biomedical devices. A compact and timely introduction, this book summarizes not only the enabling factors for soft robots and MEMS devices, but also provides a survey of progress in the field and looks to the future in terms of the material, design, and application aspects this technology demonstrates. Erscheinungsdatum: 27.12.2016

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lgli/基于机器学习和深度学习的技术中的进展.pdf

基于机器学习和深度学习的技术中的进展 it-ebooks iBooker it-ebooks, it-ebooks-extra

As the 4th Industrial Revolution is restructuring human societal organization into, so-called, " Society 5.0 ", the field of Machine Learning (and its sub-field of Deep Learning ) and related technologies is growing continuously and rapidly, developing in both itself and towards applications in many other disciplines. Researchers worldwide aim at incorporating cognitive abilities into machines, such as learning and problem solving. When machines and software systems have been enhanced with Machine Learning/Deep Learning components, they become better and more efficient at performing specific tasks. Consequently, Machine Learning/Deep Learning stands out as a research discipline due to its worldwide pace of growth in both theoretical advances and areas of application, while achieving very high rates of success and promising major impact in science, technology and society. The book at hand aims at exposing its readers to some of the most significant Advances in Machine Learning/Deep Learning-based Technologies. The book consists of an editorial note and an additional ten (10) chapters, all invited from authors who work on the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into five parts, namely (i) Machine Learning/Deep Learning in Socializing and Entertainment , (ii) Machine Learning/Deep Learning in Education , (iii) Machine Learning/Deep Learning in Security , (iv) Machine Learning/Deep Learning in Time Series Forecasting , and (v) Machine Learning in Video Coding and Information Extraction . This research book is directed towards professors, researchers, scientists, engineers and students in Machine Learning/Deep Learning-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent Machine Learning/Deep Learning-based technologies. An extensive list of bibliographic references at the end of each chapter guides the readers to probe further into the application areas of interest to them.

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zlib/Computers/Artificial Intelligence (AI)/Christopher M.Bishop, Hugh Bishop/深度学习基础与概念_118775336.pdf

深度学习基础与概念 Christopher M. Bishop; Hugh Bishop Springer Nature Switzerland AG, 2025

深度学习:基础与概念旨在为机器学习的新手以及已经有经验的人提供对深度学习基础思想和现代深度学习架构和技术的全面理解。这些材料将为读者提供未来专业化的坚实基础。由于领域的广度和变化的速度,我们故意避免试图创建最新研究的全面调查。相反,本书的价值很大程度上来自于关键思想的提炼,虽然这个领域本身可以预期会继续快速发展,但这些基础和概念很可能经得起时间的考验。例如,大型语言模型在撰写本书时发展迅速,但底层的Transformer架构和注意力机制在过去五年中基本保持不变,而许多机器学习的核心原则已经被人们熟知了几十年。

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lgli/Linear Algebra Done Right 中文第四版.pdf

Linear Algebra Done Right 中文第四版 it-ebooks iBooker it-ebooks, it-ebooks-extra

Undergraduate Texts in Mathematics Erscheinungsdatum: 20.11.2023

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lgli/浅层和深度学习原理.pdf

浅层和深度学习原理 it-ebooks iBooker it-ebooks, it-ebooks-extra

This book discusses Artificial Neural Networks (ANN) and their ability to predict outcomes using deep and shallow learning principles. The author first describes ANN implementation, consisting of at least three layers that must be established together with cells, one of which is input, the other is output, and the third is a hidden (intermediate) layer. For this, the author states, it is necessary to develop an architecture that will not model mathematical rules but only the action and response variables that control the event and the reactions that may occur within it. The book explains the reasons and necessity of each ANN model, considering the similarity to the previous methods and the philosophical - logical rules. Erscheinungsdatum: 02.06.2023

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lgli/机器和深度学习中的创新.pdf

机器和深度学习中的创新 it-ebooks iBooker it-ebooks, it-ebooks-extra

In recent years, significant progress has been made in achieving artificial intelligence (AI) with an impact on students, managers, scientists, health personnel, technical roles, investors, teachers, and leaders. This book presents numerous successful applications of AI in various contexts. The innovative implications covered fall under the general field of machine learning (ML), including deep learning, decision-making, forecasting, pattern recognition, information retrieval, and interpretable AI. Decision-makers and entrepreneurs will find numerous successful applications in health care, sustainability, risk management, human activity recognition, logistics, and Industry 4.0. This book is an essential resource for anyone interested in challenges, opportunities, and the latest developments and real-world applications of ML. Whether you are a student, researcher, practitioner, or simply curious about AI, this book provides valuable insights and inspiration for your work and learning.

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lgli/使用深度学习的基于会话的推荐系统.pdf

This book focuses on the widespread use of deep neural networks and their various techniques in session-based recommender systems (SBRS). It presents the success of using deep learning techniques in many SBRS applications from different perspectives. For this purpose, the concepts and fundamentals of SBRS are fully elaborated, and different deep learning techniques focusing on the development of SBRS are studied. The book is well-modularized, and each chapter can be read in a stand-alone manner based on individual interests and needs. In the first chapter of the book, definitions and concepts related to SBRS are reviewed, and a taxonomy of different SBRS approaches is presented, where the characteristics and applications of each class are discussed separately. The second chapter starts with the basic concepts of deep learning and the characteristics of each model. Then, each deep learning model, along with its architecture and mathematical foundations, is introduced. Next, chapter 3 analyses different approaches of deep discriminative models in session-based recommender systems. In the fourth chapter, session-based recommender systems that benefit from deep generative neural networks are discussed. Subsequently, chapter 5 discusses session-based recommender systems using advanced/hybrid deep learning models. Eventually, chapter 6 reviews different learning-to-rank methods focusing on information retrieval and recommender system domains. Finally, the results of the investigations and findings from the research review conducted throughout the book are presented in a conclusive summary. This book aims at researchers who intend to use deep learning models to solve the challenges related to SBRS. The target audience includes researchers entering the field, graduate students specializing in recommender systems, web data mining, information retrieval, or machine/deep learning, and advanced industry developers working on recommender systems.

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lgli/深度学习中的可解释性.pdf

深度学习中的可解释性 it-ebooks iBooker it-ebooks, it-ebooks-extra

This book is a comprehensive curation, exposition and illustrative discussion of recent research tools for interpretability of deep learning models, with a focus on neural network architectures. In addition, it includes several case studies from application-oriented articles in the fields of computer vision, optics and machine learning related topic. The book can be used as a monograph on interpretability in deep learning covering the most recent topics as well as a textbook for graduate students. Scientists with research, development and application responsibilities benefit from its systematic exposition.

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lgli/神经网络和深度学习教科书第二版.pdf

神经网络和深度学习教科书第二版 it-ebooks iBooker it-ebooks, it-ebooks-extra

This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Deep learning methods for various data domains, such as text, images, and graphs are presented in detail. The chapters of this book span three categories: The basics of neural networks: The backpropagation algorithm is discussed in Chapter 2.Many traditional machine learning models can be understood as special cases of neural networks. Chapter 3 explores the connections between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 4 and 5. Chapters 6 and 7 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 8, 9, and 10 discuss recurrent neural networks, convolutional neural networks, and graph neural networks. Several advanced topics like deep reinforcement learning, attention mechanisms, transformer networks, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 11 and 12. The textbook is written for graduate students and upper under graduate level students. Researchers and practitioners working within this related field will want to purchase this as well.Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.The second edition is substantially reorganized and expanded with separate chapters on backpropagation and graph neural networks. Many chapters have been significantly revised over the first edition.Greater focus is placed on modern deep learning ideas such as attention mechanisms, transformers, and pre-trained language models.

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lgli/深度学习和机器学习的分类应用.pdf

深度学习和机器学习的分类应用 it-ebooks iBooker it-ebooks, it-ebooks-extra

Studies in Computational Intelligence Erscheinungsdatum: 17.11.2022

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lgli/深度学习和其他计算技巧：生物医学及其它应该用.pdf

深度学习和其他计算技巧:生物医学及其它应该用 it-ebooks iBooker it-ebooks, it-ebooks-extra

This book focuses on the use of artificial intelligence (AI) and computational intelligence (CI) in medical and related applications. Applications include all aspects of medicine: from diagnostics (including analysis of medical images and medical data) to therapeutics (including drug design and radiotherapy) to epidemic- and pandemic-related public health policies.Corresponding techniques include machine learning (especially deep learning), techniques for processing expert knowledge (e.g., fuzzy techniques), and advanced techniques of applied mathematics (such as innovative probabilistic and graph-based techniques).The book also shows that these techniques can be used in many other applications areas, such as finance, transportation, physics. This book helps practitioners and researchers to learn more about AI and CI methods and their biomedical (and related) applications—and to further develop thisimportant research direction.

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lgli/人工智能和机器人中的关键数字趋势.pdf

人工智能和机器人中的关键数字趋势 it-ebooks iBooker it-ebooks, it-ebooks-extra

The book (proceedings of the 4th International Conference on Deep Learning, Artificial Intelligence and Robotics (ICDLAIR) 2022) introduces key topics from artificial intelligence algorithms and programming organisations and explains how they contribute to health care, manufacturing, law, finance, retail, real estate, accountancy, digital marketing, and various other fields. Although artificial intelligence (AI) has generated a lot of hype over the past ten years, these consequences on how we live, work, and play are still in their infancy and will likely have a significant impact in the future. The supremacy of AI in areas like speech and picture recognition, navigational apps, personal assistants for smartphones, ride-sharing apps, and many other areas is already well established. The book is primarily meant for academics, researchers, and engineers who want to employ AI applications to address real-world issues. The authors hope that businesses and technology creators will also find it appealing to utilise in industry.

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lgli/深度学习中的标准化技巧.pdf

深度学习中的标准化技巧 it-ebooks iBooker it-ebooks, it-ebooks-extra

This book presents and surveys normalization techniques with a deep analysis in training deep neural networks. In addition, the author provides technical details in designing new normalization methods and network architectures tailored to specific tasks. Normalization methods can improve the training stability, optimization efficiency, and generalization ability of deep neural networks (DNNs) and have become basic components in most state-of-the-art DNN architectures. The author provides guidelines for elaborating, understanding, and applying normalization methods. This book is ideal for readers working on the development of novel deep learning algorithms and/or their applications to solve practical problems in computer vision and machine learning tasks. The book also serves as a resource researchers, engineers, and students who are new to the field and need to understand and train DNNs. Erscheinungsdatum: 09.10.2022

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lgli/医学中的机器学习和深度学习基础.pdf

医学中的机器学习和深度学习基础 it-ebooks iBooker it-ebooks, it-ebooks-extra

This book provides an accessible introduction to the foundations of machine learning and deep learning in medicine for medical students, researchers, and professionals who are not necessarily initiated in advanced mathematics but yearn for a better understanding of this disruptive technology and its impact on medicine. Once an esoteric subject known to few outside of computer science and engineering departments, today artificial intelligence (AI) is a widely popular technology used by scholars from all across the academic universe. In particular, recent years have seen a great deal of interest in the AI subfields of machine learning and deep learning from researchers in medicine and life sciences, evidenced by the rapid growth in the number of articles published on the topic in peer-reviewed medical journals over the last decade. The demand for high-quality educational resources in this area has never been greater than it is today, and will only continue to grow at a rapid pace. Expert authors remove the veil of unnecessary complexity that often surrounds machine learning and deep learning by employing a narrative style that emphasizes intuition in place of abstract mathematical formalisms, allowing them to strike a delicate balance between practicality and theoretical rigor in service of facilitating the reader's learning experience. Topics covered in the book include: mathematical encoding of medical data, linear regression and classification, nonlinear feature engineering, deep learning, convolutional and recurrent neural networks, and reinforcement learning. Each chapter ends with a collection of exercises for readers to practice and test their knowledge. This is an ideal introduction for medical students, professionals, and researchers interested in learning more about machine learning and deep learning. Readers who have taken at least one introductory mathematics course at the undergraduate-level (e.g., biostatistics or calculus) will be well-equipped to use this book without needing any additional prerequisites.

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lgli/区块链和深度学习.pdf

区块链和深度学习 it-ebooks iBooker it-ebooks, it-ebooks-extra

This book introduces to blockchain and deep learning and explores and illustrates the current and new trends that integrate them. The pace and speeds for connectivity are certain on the ascend. Blockchain and deep learning are twin technologies that are integral to integrity and relevance of network contents. Since they are data-driven technologies, rapidly growing interests exist to incorporate them in efficient and secure data sharing and analysis applications. Blockchain and deep learning are sentinel contemporary research technologies. This book provides a comprehensive reference for blockchain and deep learning by covering all important topics. It identifies the bedrock principles and forward projecting methodologies that illuminate the trajectory of developments for the decades ahead.

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lgli/医疗保健中的深度学习导论.pdf

医疗保健中的深度学习导论 it-ebooks iBooker it-ebooks, it-ebooks-extra

This textbook presents deep learning models and their healthcare applications. It focuses on rich health data and deep learning models that can effectively model health data. Healthcare data: Among all healthcare technologies, electronic health records (EHRs) had vast adoption and a significant impact on healthcare delivery in recent years. One crucial benefit of EHRs is to capture all the patient encounters with rich multi-modality data. Healthcare data include both structured and unstructured information. Structured data include various medical codes for diagnoses and procedures, lab results, and medication information. Unstructured data contain 1) clinical notes as text, 2) medical imaging data such as X-rays, echocardiogram, and magnetic resonance imaging (MRI), and 3) time-series data such as the electrocardiogram (ECG) and electroencephalogram (EEG). Beyond the data collected during clinical visits, patient self-generated/reported data start to grow thanks to wearable sensors’ increasing use. The authors present deep learning case studies on all data described. Deep learning models: Neural network models are a class of machine learning methods with a long history. Deep learning models are neural networks of many layers, which can extract multiple levels of features from raw data. Deep learning applied to healthcare is a natural and promising direction with many initial successes. The authors cover deep neural networks, convolutional neural networks, recurrent neural networks, embedding methods, autoencoders, attention models, graph neural networks, memory networks, and generative models. It’s presented with concrete healthcare case studies such as clinical predictive modeling, readmission prediction, phenotyping, x-ray classification, ECG diagnosis, sleep monitoring, automatic diagnosis coding from clinical notes, automatic deidentification, medication recommendation, drug discovery (drug property prediction and molecule generation), and clinical trial matching. This textbook targets graduate-level students focused on deep learning methods and their healthcare applications. It can be used for the concepts of deep learning and its applications as well. Researchers working in this field will also find this book to be extremely useful and valuable for their research.

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lgli/机器学习的变分方法及其在深度网络中的应用.pdf

机器学习的变分方法及其在深度网络中的应用 it-ebooks iBooker it-ebooks, it-ebooks-extra

This book provides a straightforward look at the concepts, algorithms and advantages of Bayesian Deep Learning and Deep Generative Models. Starting from the model-based approach to Machine Learning, the authors motivate Probabilistic Graphical Models and show how Bayesian inference naturally lends itself to this framework. The authors present detailed explanations of the main modern algorithms on variational approximations for Bayesian inference in neural networks. Each algorithm of this selected set develops a distinct aspect of the theory. The book builds from the ground-up well-known deep generative models, such as Variational Autoencoder and subsequent theoretical developments. By also exposing the main issues of the algorithms together with different methods to mitigate such issues, the book supplies the necessary knowledge on generative models for the reader to handle a wide range of data types: sequential or not, continuous or not, labelled or not. The book is self-contained, promptly covering all necessary theory so that the reader does not have to search for additional information elsewhere. Offers a concise self-contained resource, covering the basic concepts to the algorithms for Bayesian Deep Learning; Presents Statistical Inference concepts, offering a set of elucidative examples, practical aspects, and pseudo-codes; Every chapter includes hands-on examples and exercises and a website features lecture slides, additional examples, and other support material.

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lgli/深度学习架构的开发和分析.pdf

深度学习架构的开发和分析 it-ebooks iBooker it-ebooks, it-ebooks-extra

This book offers a timely reflection on the remarkable range of algorithms and applications that have made the area of deep learning so attractive and heavily researched today. Introducing the diversity of learning mechanisms in the environment of big data, and presenting authoritative studies in fields such as sensor design, health care, autonomous driving, industrial control and wireless communication, it enables readers to gain a practical understanding of design. The book also discusses systematic design procedures, optimization techniques, and validation processes. Erscheinungsdatum: 13.11.2019

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lgli/人工心理学导论.pdf

人工心理学导论 it-ebooks iBooker it-ebooks, it-ebooks-extra

Artificial Psychology (AP) is a highly multidisciplinary field of study in psychology. AP tries to solve problems which occur when psychologists do research and need a robust analysis method. Conventional statistical approaches have deep rooted limitations. These approaches are excellent on paper but often fail to model the real world. Mind researchers have been trying to overcome this by simplifying the models being studied. This stance has not received much practical attention recently. Promoting and improving artificial intelligence helps mind researchers to find a holistic model of mental models. This development achieves this goal by using multiple perspectives and multiple data sets together with interactive, and realistic models. In this book, the methodology of approximate inference in psychological research from a theoretical and practical perspective has been considered. Quantitative variable-oriented methodology and qualitative case-oriented methods are both used to explainthe set-oriented methodology and this book combines the precision of quantitative methods with information from qualitative methods. This is a book that many researchers can use to expand and deepen their psychological research and is a book which can be useful to postgraduate students. The reader does not need an in-depth knowledge of mathematics or statistics because statistical and mathematical intuitions are key here and they will be learned through practice. What is important is to understand and use the new application of the methods for finding new, dynamic and realistic interpretations. This book incorporates theoretical fuzzy inference and deep machine learning algorithms in practice. This is the kind of book that we wished we had had when we were students. This book covers at least some of the most important issues in mind research including uncertainty, fuzziness, continuity, complexity and high dimensionality which are inherent to mind data. These are elements of artificialpsychology. This book implements models using R software. Erscheinungsdatum: 19.05.2023

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lgli/人工智能和国家安全.pdf

人工智能和国家安全 it-ebooks iBooker it-ebooks, it-ebooks-extra

This book analyses the implications of the technical, legal, ethical and privacy challenges as well as challenges for human rights and civil liberties regarding Artificial Intelligence (AI) and National Security. It also offers solutions that can be adopted to mitigate or eradicate these challenges wherever possible. As a general-purpose, dual-use technology, AI can be deployed for both good and evil. The use of AI is increasingly becoming of paramount importance to the government's mission to keep their nations safe. However, the design, development and use of AI for national security poses a wide range of legal, ethical, moral and privacy challenges. This book explores national security uses for Artificial Intelligence (AI) in Western Democracies and its malicious use. This book also investigates the legal, political, ethical, moral, privacy and human rights implications of the national security uses of AI in the aforementioned democracies. It illustrates how AI for national security purposes could threaten most individual fundamental rights, and how the use of AI in digital policing could undermine user human rights and privacy. In relation to its examination of the adversarial uses of AI, this book discusses how certain countries utilise AI to launch disinformation attacks by automating the creation of false or misleading information to subvert public discourse. With regards to the potential of AI for national security purposes, this book investigates how AI could be utilized in content moderation to counter violent extremism on social media platforms. It also discusses the current practices in using AI in managing Big Data Analytics demands. This book provides a reference point for researchers and advanced-level students studying or working in the fields of Cyber Security, Artificial Intelligence, Social Sciences, Network Security as well as Law and Criminology. Professionals working within these related fields and law enforcement employees will also find this book valuable as a reference.

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lgli/Stefan Staicu - 并联机器人动力学 (2018, Springer).epub

并联机器人动力学 Ștefan Staicu Springer International Publishing : Imprint : Springer, Springer Nature, Cham, 2018

This book establishes recursive relations concerning kinematics and dynamics of constrained robotic systems. It uses matrix modeling to determine the connectivity conditions on the relative velocities and accelerations in order to compare two efficient energetic ways in dynamics modeling: the principle of virtual work, and the formalism of Lagrange's equations. First, a brief fundamental theory is presented on matrix mechanics of the rigid body, which is then developed in the following five chapters treating matrix kinematics of the rigid body, matrix kinematics of the composed motion, kinetics of the rigid body, dynamics of the rigid body, and analytical mechanics. By using a set of successive mobile frames, the geometrical properties and the kinematics of the vector system of velocities and accelerations for each element of the robot are analysed. The dynamics problem is solved in two energetic ways: using an approach based on the principle of virtual work and applying the formalism of Lagrange's equations of the second kind. These are shown to be useful for real-time control of the robot's evolution. Then the recursive matrix method is applied to the kinematics and dynamics analysis of five distinct case studies: planar parallel manipulators, spatial parallel robots, planetary gear trains, mobile wheeled robots and, finally, two-module hybrid parallel robots.

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lgli/Jadran Lenarcic - 机器人运动学进展 (2019, Springer).epub

机器人运动学进展 Jadran Lenarcic; Vincenzo Parenti-Castelli Springer International Publishing : Imprint : Springer, Springer Proceedings in Advanced Robotics Ser, Cham, 2018

This is the proceedings of ARK 2018, the 16th International Symposium on Advances in Robot Kinematics, that was organized by the Group of Robotics, Automation and Biomechanics (GRAB) from the University of Bologna, Italy. ARK are international symposia of the highest level organized every two years since 1988. ARK provides a forum for researchers working in robot kinematics and stimulates new directions of research by forging links between robot kinematics and other areas. The main topics of the symposium of 2018 were: kinematic analysis of robots, robot modeling and simulation, kinematic design of robots, kinematics in robot control, theories and methods in kinematics, singularity analysis, kinematic problems in parallel robots, redundant robots, cable robots, over-constrained linkages, kinematics in biological systems, humanoid robots and humanoid subsystems.

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lgli/南来顺，京城吃货日记 - 北京小吃 / BEIJINGXIAOCHI 1(1980, 天津科学技术出版社).pdf

北京小吃 / BEIJINGXIAOCHI 1 1 南来顺,京城吃货日记天津科学技术出版社, 1, 1, 1980

北京小吃具有悠久的历史。由于它品种多、做工细、色香味形俱佳;风味独特、适应民俗、经济实惠,所以深受广大人民欢迎。

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lgli/3D点云分析.pdf

3D点云分析 it-ebooks iBooker it-ebooks, it-ebooks-extra

This book introduces the point cloud; its applications in industry, and the most frequently used datasets. It mainly focuses on three computer vision tasks -- point cloud classification, segmentation, and registration -- which are fundamental to any point cloud-based system. An overview of traditional point cloud processing methods helps readers build background knowledge quickly, while the deep learning on point clouds methods include comprehensive analysis of the breakthroughs from the past few years. Brand-new explainable machine learning methods for point cloud learning, which are lightweight and easy to train, are then thoroughly introduced. Quantitative and qualitative performance evaluations are provided. The comparison and analysis between the three types of methods are given to help readers have a deeper understanding. With the rich deep learning literature in 2D vision, a natural inclination for 3D vision researchers is to develop deep learning methods for point cloud processing. Deep learning on point clouds has gained popularity since 2017, and the number of conference papers in this area continue to increase. Unlike 2D images, point clouds do not have a specific order, which makes point cloud processing by deep learning quite challenging. In addition, due to the geometric nature of point clouds, traditional methods are still widely used in industry. Therefore, this book aims to make readers familiar with this area by providing comprehensive overview of the traditional methods and the state-of-the-art deep learning methods. A major portion of this book focuses on explainable machine learning as a different approach to deep learning. The explainable machine learning methods offer a series of advantages over traditional methods and deep learning methods. This is a main highlight and novelty of the book. By tackling three research tasks -- 3D object recognition, segmentation, and registration using our methodology -- readers will have a sense of how to solve problems in a different way and can apply the frameworks to other 3D computer vision tasks, thus give them inspiration for their own future research. Numerous experiments, analysis and comparisons on three 3D computer vision tasks (object recognition, segmentation, detection and registration) are provided so that readers can learn how to solve difficult Computer Vision problems.

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lgli/深度学习应用手册.pdf

深度学习应用手册 it-ebooks iBooker it-ebooks, it-ebooks-extra

"This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain-computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars." -- prové de l'editor

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lgli/带有性能保障的深度强化学习.pdf

带有性能保障的深度强化学习 it-ebooks iBooker it-ebooks, it-ebooks-extra

"This book discusses methods and algorithms for the near-optimal adaptive control of nonlinear systems, including the corresponding theoretical analysis and simulative examples, and presents two innovative methods for the redundancy resolution of redundant manipulators with consideration of parameter uncertainty and periodic disturbances. It also reports on a series of systematic investigations on a near-optimal adaptive control method based on the Taylor expansion, neural networks, estimator design approaches, and the idea of sliding mode control, focusing on the tracking control problem of nonlinear systems under different scenarios. The book culminates with a presentation of two new redundancy resolution methods; one addresses adaptive kinematic control of redundant manipulators, and the other centers on the effect of periodic input disturbance on redundancy resolution. Each self-contained chapter is clearly written, making the book accessible to graduate students as well as academic and industrial researchers in the fields of adaptive and optimal control, robotics, and dynamic neural networks." - Prové de l'editor

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lgli/基于深度学习的语音质量预测.pdf

基于深度学习的语音质量预测 it-ebooks iBooker it-ebooks, it-ebooks-extra

This book presents how to apply recent machine learning (deep learning) methods for the task of speech quality prediction. The author shows how recent advancements in machine learning can be leveraged for the task of speech quality prediction and provides an in-depth analysis of the suitability of different deep learning architectures for this task. The author then shows how the resulting model outperforms traditional speech quality models and provides additional information about the cause of a quality impairment through the prediction of the speech quality dimensions of noisiness, coloration, discontinuity, and loudness. Erscheinungsdatum: 25.02.2022

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lgli/医疗保健中的深度学习.pdf

医疗保健中的深度学习 it-ebooks iBooker it-ebooks, it-ebooks-extra

"This book provides a comprehensive overview of deep learning (DL) in medical and healthcare applications, including the fundamentals and current advances in medical image analysis, state-of-the-art DL methods for medical image analysis and real-world, deep learning-based clinical computer-aided diagnosis systems. Deep learning (DL) is one of the key techniques of artificial intelligence (AI) and today plays an important role in numerous academic and industrial areas. DL involves using a neural network with many layers (deep structure) between input and output, and its main advantage of is that it can automatically learn data-driven, highly representative and hierarchical features and perform feature extraction and classification on one network. DL can be used to model or simulate an intelligent system or process using annotated training data. Recently, DL has become widely used in medical applications, such as anatomic modelling, tumour detection, disease classification, computer-aided diagnosis and surgical planning. This book is intended for computer science and engineering students and researchers, medical professionals and anyone interested using DL techniques." - Prové de l'editor

英语 [en] · 中文 [zh] · PDF · 8.8MB · 2020 · 📘 非小说类图书 · 🚀/lgli/lgrs · Save

lgli/使用人工智能和深度学习的恶意软件分析.pdf

使用人工智能和深度学习的恶意软件分析 it-ebooks iBooker it-ebooks, it-ebooks-extra

This book is focused on the use of deep learning (DL) and artificial intelligence (AI) as tools to advance the fields of malware detection and analysis. The individual chapters of the book deal with a wide variety of state-of-the-art AI and DL techniques, which are applied to a number of challenging malware-related problems. DL and AI based approaches to malware detection and analysis are largely data driven and hence minimal expert domain knowledge of malware is needed. This book fills a gap between the emerging fields of DL/AI and malware analysis. It covers a broad range of modern and practical DL and AI techniques, including frameworks and development tools enabling the audience to innovate with cutting-edge research advancements in a multitude of malware (and closely related) use cases.

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lgli/对抗性机器学习.pdf

对抗性机器学习 it-ebooks iBooker it-ebooks, it-ebooks-extra

A critical challenge in deep learning is the vulnerability of deep learning networks to security attacks from intelligent cyber adversaries. Even innocuous perturbations to the training data can be used to manipulate the behaviour of deep networks in unintended ways. In this book, we review the latest developments in adversarial attack technologies in computer vision; natural language processing; and cybersecurity with regard to multidimensional, textual and image data, sequence data, and temporal data. In turn, we assess the robustness properties of deep learning networks to produce a taxonomy of adversarial examples that characterises the security of learning systems using game theoretical adversarial deep learning algorithms. The state-of-the-art in adversarial perturbation-based privacy protection mechanisms is also reviewed. We propose new adversary types for game theoretical objectives in non-stationary computational learning environments. Proper quantification of the hypothesis set in the decision problems of our research leads to various functional problems, oracular problems, sampling tasks, and optimization problems. We also address the defence mechanisms currently available for deep learning models deployed in real-world environments. The learning theories used in these defence mechanisms concern data representations, feature manipulations, misclassifications costs, sensitivity landscapes, distributional robustness, and complexity classes of the adversarial deep learning algorithms and their applications. In closing, we propose future research directions in adversarial deep learning applications for resilient learning system design and review formalized learning assumptions concerning the attack surfaces and robustness characteristics of artificial intelligence applications so as to deconstruct the contemporary adversarial deep learning designs. Given its scope, the book will be of interest to Adversarial Machine Learning practitioners and Adversarial Artificial Intelligence researchers whose work involves the design and application of Adversarial Deep Learning.

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nexusstc/Dao Companion to ZHU Xi’s Philosophy/63d38cb5afe3550702a627e841acd579.epub

Dao Companion to ZHU Xi’s Philosophy (Dao Companions to Chinese Philosophy, 13) Kai-chiu Ng; Yong Huang; SpringerLink (Online service) Springer International Publishing : Imprint: Springer, Dao Companions to Chinese Philosophy, Dao Companions to Chinese Philosophy, 1, 2020

Zhu Xi (1130-1200) has been commonly and justifiably recognized as the most influential philosopher of Neo-Confucianism, a revival of classical Confucianism in face of the challenges coming from Daoism and, more importantly, Buddhism. His place in the Confucian tradition is often and also very plausibly compared to that of Thomas Aquinas, slightly later, in the Christian tradition. This book presents the most comprehensive and updated study of this great philosopher. It situates Zhu Xi{u2019}s philosophy in the historical context of not only Confucian philosophy but also Chinese philosophy as a whole. Topics covered within Zhu Xi{u2019}s thought are metaphysics, epistemology, ethics, political philosophy, hermeneutics, philosophy of religion, moral psychology, and moral education. This text shows both how Zhu Xi responded to earlier thinkers and how his thoughts resonate in contemporary philosophy, particularly in the analytic tradition. This companion will appeal to students, researchers and educators in the field

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lgli/Patrick Grosch - 具有非常规关节的并行机器人 (2019, Springer).epub

具有非常规关节的并行机器人 Patrick Grosch; Federico Thomas Springer International Publishing : Imprint : Springer, Springer Nature, Cham, Switzerland, 2019

This book shows how, through certain geometric transformations, some of the standard joints used in parallel robots can be replaced with lockable or non-holonomic joints. These substitutions allow for reducing the number of legs, and hence the number of actuators needed to control the robot, without losing the robot's ability to bring its mobile platform to the desired configuration. The kinematics of the most representative examples of these new designs are analyzed and their theoretical features verified through simulations and practical implementations.

英语 [en] · 中文 [zh] · EPUB · 1.8MB · 2019 · 📘 非小说类图书 · 🚀/lgli/zlib · Save

lgli/基于深度学习的人脸分析.pdf

基于深度学习的人脸分析 it-ebooks iBooker it-ebooks, it-ebooks-extra

This book provides an overview of different deep learning-based methods for face recognition and related problems. Specifically, the authors present methods based on autoencoders, restricted Boltzmann machines, and deep convolutional neural networks for face detection, localization, tracking, recognition, etc. The authors also discuss merits and drawbacks of available approaches and identifies promising avenues of research in this rapidly evolving field. Even though there have been a number of different approaches proposed in the literature for face recognition based on deep learning methods, there is not a single book available in the literature that gives a complete overview of these methods. The proposed book captures the state of the art in face recognition using various deep learning methods, and it covers a variety of different topics related to face recognition. This book is aimed at graduate students studying electrical engineering and/or computer science. Biometrics is a course that is widely offered at both undergraduate and graduate levels at many institutions around the world: This book can be used as a textbook for teaching topics related to face recognition. In addition, the work is beneficial to practitioners in industry who are working on biometrics-related problems. The prerequisites for optimal use are the basic knowledge of pattern recognition, machine learning, probability theory, and linear algebra.

英语 [en] · 中文 [zh] · PDF · 16.6MB · 2021 · 📘 非小说类图书 · 🚀/lgli/lgrs · Save

lgli/Jakob Schwichtenberg - 基于对称性的现代物理学 (2018, 超理汉化组).pdf

基于对称性的现代物理学 Jakob Schwichtenberg 超理汉化组, 2018

原著:Jakob Schwichtenberg 翻译:超理汉化组Translate Version: 2018 年 4 月 9 日本文档由热心网友翻译制作,原著书名为:Physics from Symmetry,属 Springer公司 Undergraduate Lecture Notes in Physics(ULNP) 丛书,ISBN 978-3-319-19200-0,DOI 10.1007/978-3-319-19201-7。

英语 [en] · 中文 [zh] · PDF · 3.2MB · 2018 · 📘 非小说类图书 · 🚀/lgli/zlib · Save

lgli/Vassilis M. Charitopoulos - Uncertainty-aware Integration of Control with Process Operations and Multi-parametric Programming Under Global Uncertainty (2020, Springer).fb2

Uncertainty-aware Integration Of Control With Process Operations And Multi-parametric Programming Under Global Uncertainty (springer Theses) Vassilis M Charitopoulos; SpringerLink (Online service) Springer International Publishing, Imprint Springer, Springer Nature, Cham, 2020

This book introduces models and methodologies that can be employed towards making the Industry 4.0 vision a reality within the process industries, and at the same time investigates the impact of uncertainties in such highly integrated settings. Advances in computing power along with the widespread availability of data have led process industries to consider a new paradigm for automated and more efficient operations. The book presents a theoretically proven optimal solution to multi-parametric linear and mixed-integer linear programs and efficient solutions to problems such as process scheduling and design under global uncertainty. It also proposes a systematic framework for the uncertainty-aware integration of planning, scheduling and control, based on the judicious coupling of reactive and proactive methods. Using these developments, the book demonstrates how the integration of different decision-making layers and their simultaneous optimisation can enhance industrial process operations and their economic resilience in the face of uncertainty. Erscheinungsdatum: 05.02.2020

英语 [en] · 中文 [zh] · FB2 · 3.3MB · 2020 · 📕 小说类图书 · 🚀/lgli/zlib · Save