deep learning algorithms applications (48 Ergebnisse)

XII, 360 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Studies in Computational Intelligence, 865. Sprache: Englisch.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces basic machine learning concepts and applications for a broad audience that includes students, faculty, and industry practitioners. We begin by describing how machine learning provides capabilities to computers and embedded systems to learn from data. A typical machine learning algorithm involves training, and generally the performance of a machine learning model improves with more training data. Deep learning is a sub-area of machine learning that involves extensive use of layers of artificial neural networks typically trained on massive amounts of data. Machine and deep learning methods are often used in contemporary data science tasks to address the growing data sets and detect, cluster, and classify data patterns. Although machine learning commercial interest has grown relatively recently, the roots of machine learning go back to decades ago. We note that nearly all organizations, including industry, government, defense, and health, are using machine learning toaddress a variety of needs and applications. The machine learning paradigms presented can be broadly divided into the following three categories: supervised learning, unsupervised learning, and semi-supervised learning. Supervised learning algorithms focus on learning a mapping function, and they are trained with supervision on labeled data. Supervised learning is further sub-divided into classification and regression algorithms. Unsupervised learning typically does not have access to ground truth, and often the goal is to learn or uncover the hidden pattern in the data. Through semi-supervised learning, one can effectively utilize a large volume of unlabeled data and a limited amount of labeled data to improve machine learning model performances. Deep learning and neural networks are also covered in this book. Deep neural networks have attracted a lot of interest during the last ten years due to the availability of graphics processing units (GPU) computational power, big data, and new software platforms. They have strong capabilities in terms of learning complex mapping functions for different types of data. We organize the book as follows. The book starts by introducing concepts in supervised, unsupervised, and semi-supervised learning. Several algorithms and their inner workings are presented within these three categories. We then continue with a brief introduction to artificial neural network algorithms and their properties. In addition, we cover an array of applications and provide extensive bibliography. The book ends with a summary of the key machine learning concepts.

Zustand: New. In.

Zustand: New. In.

Zustand: Gut. Zustand: Gut | Seiten: 192 | Sprache: Englisch | Produktart: Bücher.

Taschenbuch. Zustand: Neu. Neuware -A book that covers different machine learning and deep learning algorithms specifically for lung cancer would be an excellent resource for researchers, medical professionals, and data scientists interested in leveraging artificial intelligence for medical diagnosis and treatment. While I can't provide a specific book title, I can suggest the key topics and areas such a book might cover: Introduction to Lung Cancer: The book may start with an overview of lung cancer, including its types, causes, diagnosis methods, and current treatment options. Machine Learning Fundamentals: It may cover fundamental concepts of machine learning, including supervised, unsupervised, and semi-supervised learning, as well as evaluation metrics commonly used in medical applications.Data Preprocessing and Feature Engineering: The book may discuss techniques for preprocessing medical imaging data, such as CT scans or X-rays, including image normalization, noise reduction, and feature extraction.Classification Algorithms: It would likely delve into various classification algorithms used for lung cancer detection and diagnosis, such as support vector machines (SVM), random forests, decision trees.Books on Demand GmbH, Überseering 33, 22297 Hamburg 184 pp. Englisch.

Zustand: New. In.

Paperback. Zustand: Brand New. 412 pages. 9.00x7.25x0.75 inches. In Stock.

Hardcover. Zustand: Brand New. 2nd edition. 242 pages. 9.25x6.10x9.21 inches. In Stock.

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 344 | Sprache: Englisch | Produktart: Bücher.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents new technologies and applications in deep learning, artificial intelligence and robotics. The field of machine intelligence (MI), unifying robotics and artificial intelligence is experiencing constant growth and change. The challenge to reproduce human behavior in machines requires the interaction of many fields, from engineering to mathematics, from neurology to biology, from computer science to robotics, from web search to social networks, from machine learning to game theory, etc.This book Progresses in Artificial Intelligence & Robotics : Algorithms & Applications (proceedings of 3rd International Conference on Deep Learning, Artificial Intelligence and Robotics (ICDLAIR) 2021 ) introduces key topics from artificial intelligence algorithms and programming organization and explains how they contribute to autonomous capabilities. The book is primarily intended for researchers, students, and engineers who wish to use the applications of artificial intelligence to solve concrete problems. We hope that companies and technology developers also find it interesting to be used in industry.

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm's algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.

Zustand: New. In.

Zustand: New. In.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

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Buch. Zustand: Neu. Neuware - FUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNINGThe book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications.Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software applications development. Also, in recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field.The goal of this book is to present a practical approach by explaining the concepts of machine learning and deep learning algorithms with applications. Supervised machine learning algorithms, ensemble machine learning algorithms, feature selection, deep learning techniques, and their applications are discussed. Also included in the eighteen chapters is unique information which provides a clear understanding of concepts by using algorithms and case studies illustrated with applications of machine learning and deep learning in different domains, including disease prediction, software defect prediction, online television analysis, medical image processing, etc. Each of the chapters briefly described below provides both a chosen approach and its implementation.AudienceResearchers and engineers in artificial intelligence, computer scientists as well as software developers.

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