Автор: Lars Eld?n Название: Matrix Methods in Data Mining and Pattern Recognition ISBN: 0898716268 ISBN-13(EAN): 9780898716269 Издательство: Cambridge Academ Рейтинг: Цена: 60190.00 T Наличие на складе: Поставка под заказ. Описание: Several very powerful numerical linear algebra techniques are available for solving problems in data mining and pattern recognition. This application-oriented book describes how modern matrix methods can be used to solve these problems, gives an introduction to matrix theory and decompositions, and provides students with a set of tools that can be modified for a particular application. Part I gives a short introduction to a few application areas before presenting linear algebra concepts and matrix decompositions that students can use in problem-solving environments such as MATLAB. In Part II, linear algebra techniques are applied to data mining problems. Part III is a brief introduction to eigenvalue and singular value algorithms. The applications discussed include classification of handwritten digits, text mining, text summarization, pagerank computations related to the Google search engine, and face recognition. Exercises and computer assignments are available on a Web page that supplements the book.
Medical Treatment, Imaging and Analysis.- Image Registration, Denoising and Feature Identification.- Image Segmentation.- Shape Analysis, Meshing and Graphs.- Medical Image Processing and Simulations.- Image Recognition, Reconstruction and Predictive Modeling.- Image-Based Modeling and Simulations.- Computer Vision and Data-Driven Investigations.
Автор: Kreutzer Ralf T., Sirrenberg Marie Название: Understanding Artificial Intelligence: Fundamentals, Use Cases and Methods for a Corporate AI Journey ISBN: 3030252736 ISBN-13(EAN): 9783030252731 Издательство: Springer Цена: 53100.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Artificial Intelligence (AI) will change the lives of people and businesses more fundamentally than many people can even imagine today. In turn, the book assesses the potential that AI holds, and clarifies the framework that is necessary for pursuing a responsible approach to AI.
Автор: Singh, Pardeep Название: Fundamentals and methods of machine and deep learning ISBN: 1119821258 ISBN-13(EAN): 9781119821250 Издательство: Wiley Рейтинг: Цена: 197420.00 T Наличие на складе: Поставка под заказ. Описание: Aimed at obstetric and pediatric anesthesiologists, this practical book explores the different defects treated during pregnancy, providing the knowledge and clinical pearls to care for mother and fetus during these procedures. It covers the nuances of the diagnoses, pathophysiology and anesthetic management of patients presenting for fetal surgery.
Автор: Andrade Название: Fundamentals of Stream Processing ISBN: 1107015545 ISBN-13(EAN): 9781107015548 Издательство: Cambridge Academ Рейтинг: Цена: 91870.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book teaches fundamentals of the stream processing paradigm that addresses performance, scalability and usability challenges in extracting insights from massive amounts of live, streaming data. It presents core principles behind application design, system infrastructure and analytics, coupled with real-world examples for a comprehensive understanding of the stream processing area.
Автор: Beaulieu Alan Название: Learning SQL: Master SQL Fundamentals, 3 ed. ISBN: 1492057614 ISBN-13(EAN): 9781492057611 Издательство: Wiley Рейтинг: Цена: 55960.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: As data floods into your company, you need to put it to work right away-and SQL is the best tool for the job. With the latest edition of this introductory guide, author Alan Beaulieu helps developers get up to speed with SQL fundamentals for writing database applications, performing administrative tasks, and generating reports.
Автор: Robert Stackowiak Название: Azure Internet of Things Revealed ISBN: 1484254694 ISBN-13(EAN): 9781484254691 Издательство: Springer Рейтинг: Цена: 51230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Design, build, and justify an optimal Microsoft IoT footprint to meet your project needs. This book describes common Internet of Things components and architecture and then focuses on Microsoft’s Azure components relevant in deploying these solutions.
Microsoft-specific topics addressed include: deploying edge devices and pushing intelligence to the edge; connecting IoT devices to Azure and landing data there, applying Azure Machine Learning, analytics, and Cognitive Services; roles for Microsoft solution accelerators and managed solutions; and integration of the Azure footprint with legacy infrastructure.
The book concludes with a discussion of best practices in defining and developing solutions and creating a plan for success.
What You Will Learn
Design the right IoT architecture to deliver solutions for a variety of project needsConnect IoT devices to Azure for data collection and delivery of servicesUse Azure Machine Learning and Cognitive Services to deliver intelligence in cloud-based solutions and at the edgeUnderstand the benefits and tradeoffs of Microsoft's solution accelerators and managed solutionsInvestigate new use cases that are described and apply best practices in deployment strategiesIntegrate cutting-edge Azure deployments with existing legacy data sources
Who This Book Is For
Developers and architects new to IoT projects or new to Microsoft Azure IoT components as well as readers interested in best practices used in architecting IoT solutions that utilize the Azure platform
Автор: Quaintance Jocelyn, Gallier Jean H Название: Linear Algebra And Optimization With Applications To Machine Learning - Volume Ii: Fundamentals Of Optimization Theory With Applications To Machine Learning ISBN: 9811216568 ISBN-13(EAN): 9789811216565 Издательство: World Scientific Publishing Рейтинг: Цена: 190080.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Volume 2 applies the linear algebra concepts presented in Volume 1 to optimization problems which frequently occur throughout machine learning. This book blends theory with practice by not only carefully discussing the mathematical under pinnings of each optimization technique but by applying these techniques to linear programming, support vector machines (SVM), principal component analysis (PCA), and ridge regression. Volume 2 begins by discussing preliminary concepts of optimization theory such as metric spaces, derivatives, and the Lagrange multiplier technique for finding extrema of real valued functions. The focus then shifts to the special case of optimizing a linear function over a region determined by affine constraints, namely linear programming. Highlights include careful derivations and applications of the simplex algorithm, the dual-simplex algorithm, and the primal-dual algorithm. The theoretical heart of this book is the mathematically rigorous presentation of various nonlinear optimization methods, including but not limited to gradient decent, the Karush-Kuhn-Tucker (KKT) conditions, Lagrangian duality, alternating direction method of multipliers (ADMM), and the kernel method. These methods are carefully applied to hard margin SVM, soft margin SVM, kernel PCA, ridge regression, lasso regression, and elastic-net regression. Matlab programs implementing these methods are included.
Автор: Nagy Zsolt Название: Artificial Intelligence and Machine Learning Fundamentals ISBN: 1789801656 ISBN-13(EAN): 9781789801651 Издательство: Неизвестно Рейтинг: Цена: 40450.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Artificial Intelligence and Machine Learning Fundamentals teaches you machine learning and neural networks from the ground up using real-world examples. After you complete this book, you will be excited to revamp your current projects or build new intelligent networks.
Автор: Jes?s Polo; Luis Mart?n-Pomares; Antonio Sanfilipp Название: Solar Resources Mapping ISBN: 3319974831 ISBN-13(EAN): 9783319974835 Издательство: Springer Рейтинг: Цена: 111790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
This book presents methods for optimising the spatial and network configuration of solar radiation measuring stations. Various physical and mathematical models are demonstrated, which together with high quality measurements, provide the essential tools to generate and validate solar resource estimates to improve the mapping of solar resources.
Each chapter deals with a specific topic, showing its methodology, and providing examples of how to apply these techniques with reference to current projects around the world. These topics include:
· Radiometric measurement campaigns;
· Equipment calibration, installation, operation, and maintenance;
· Data quality assurance and assessment;
· Solar radiation modelling from satellite images and numerical models;
· Downscaling and kriging interpolation of solar radiation;
· Simulation of electric solar power plant generation;
· Solar radiation forecasting;
· Applications of solar energy; and
· Socio-economic benefits of solar energy.
The contributors present the statistical and physical models needed to derive solar radiation from satellite images and numerical models, emphasising the importance of measuring solar radiation accurately. They also show the classical models used to generate synthetic data, clear sky models and ancillary air quality and meteorological data from different input sources.
Solar Resources Mapping provides industry professionals with methodologies and tools to build solar irradiance maps for different applications. The book will also benefit students and researchers as it serves as a main technical reference, presenting the basic terminology and fundamentals for solar resource mapping that include methods for assessing measurement uncertainty.
Автор: Yao Название: Nanophotonics and Machine Learning ISBN: 3031204727 ISBN-13(EAN): 9783031204722 Издательство: Springer Рейтинг: Цена: 111790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book, the first of its kind, bridges the gap between the increasingly interlinked fields of nanophotonics and artificial intelligence (AI). While artificial intelligence techniques, machine learning in particular, have revolutionized many different areas of scientific research, nanophotonics holds a special position as it simultaneously benefits from AI-assisted device design whilst providing novel computing platforms for AI. This book is aimed at both researchers in nanophotonics who want to utilize AI techniques and researchers in the computing community in search of new photonics-based hardware. The book guides the reader through the general concepts and specific topics of relevance from both nanophotonics and AI, including optical antennas, metamaterials, metasurfaces, and other photonic devices on the one hand, and different machine learning paradigms and deep learning algorithms on the other. It goes on to comprehensively survey inverse techniques for device design, AI-enabled applications in nanophotonics, and nanophotonic platforms for AI. This book will be essential reading for graduate students, academic researchers, and industry professionals from either side of this fast-developing, interdisciplinary field.
Автор: Borhani Название: Fundamentals of Machine Learning and Deep Learning in Medicine ISBN: 3031195019 ISBN-13(EAN): 9783031195013 Издательство: Springer Рейтинг: Цена: 83850.00 T Наличие на складе: Нет в наличии. Описание: 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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