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Condition: As New. Unread book in perfect condition.
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Advanced Research and Applications of Deep Learning and Neural Network in Image Recognition
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Advanced Research and Applications of Deep Learning and Neural Network in Image Recognition
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Advanced Research and Applications of Deep Learning and Neural Network in Image Recognition
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Deep Learning: Research and Applications (De Gruyter Frontiers in Computational Intelligence, 7)
Book 1 of 14: De Gruyter Frontiers in Computational IntelligenceSeller: Ria Christie Collections, Uxbridge, United Kingdom
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Novel Financial Applications of Machine Learning and Deep Learning: Algorithms, Product Modeling, and Applications (International Series in Operations Research & Management Science, 336)
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep learning is a new area of machine learning research which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non-immediately related fields, for example between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g. classification) and/or unsupervised (e.g. pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition.
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Deep Learning Applications in Operations Research
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
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Condition: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep learning is a new area of machine learning research which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non-immediately related fields, for example between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g. classification) and/or unsupervised (e.g. pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition.
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Paperback. Condition: Brand New. 541 pages. 9.25x6.10x9.05 inches. In Stock.
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Deep Learning Applications in Operations Research
Language: English
Published by Auerbach Publications, 2024
ISBN 10: 1032708026 ISBN 13: 9781032708027
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Deep Learning Research Applications for Natural Language Processing
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Advanced Interdisciplinary Applications of Deep Learning for Data Science (Premier Research Source)
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Deep Learning Research Applications for Natural Language Processing
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Deep Learning Applications in Operations Research (Advances in Computational Collective Intelligence)
Language: English
Published by Auerbach Publications, 2024
ISBN 10: 1032708026 ISBN 13: 9781032708027
Seller: Books From California, Simi Valley, CA, U.S.A.
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hardcover. Condition: Fine.
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Deep Reinforcement Learning : Fundamentals, Research and Applications
Language: English
Published by Springer Nature Singapore, 2020
ISBN 10: 9811540942 ISBN 13: 9789811540943
Condition: Sehr gut. Zustand: Sehr gut | Seiten: 544 | Sprache: Englisch | Produktart: Bücher | Deep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. It has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine, and famously contributed to the success of AlphaGo. Furthermore, it opens up numerous new applications in domains such as healthcare, robotics, smart grids and finance. Divided into three main parts, this book provides a comprehensive and self-contained introduction to DRL. The first part introduces the foundations of deep learning, reinforcement learning (RL) and widely used deep RL methods and discusses their implementation. The second part covers selected DRL research topics, which are useful for those wanting to specialize in DRL research. To help readers gain a deep understanding of DRL and quickly apply the techniques in practice, the third part presents mass applications, such as the intelligent transportation system and learning to run, with detailedexplanations. The book is intended for computer science students, both undergraduate and postgraduate, who would like to learn DRL from scratch, practice its implementation, and explore the research topics. It also appeals to engineers and practitioners who do not have strong machine learning background, but want to quickly understand how DRL works and use the techniques in their applications.
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Condition: As New. Unread book in perfect condition.
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Condition: New.
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Deep Learning Applications in Operations Research
Language: English
Published by Auerbach Publications, 2024
ISBN 10: 1032708026 ISBN 13: 9781032708027
Condition: As New. Unread book in perfect condition.
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Deep Learning Research Applications for Natural Language Processing
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Deep Learning Research Applications for Natural Language Processing
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
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Add to basketPaperback. Condition: New. Humans have the most advanced method of communication, which is known as natural language. While humans can use computers to send voice and text messages to each other, computers do not innately know how to process natural language. In recent years, deep learning has primarily transformed the perspectives of a variety of fields in artificial intelligence (AI), including speech, vision, and natural language processing (NLP). The extensive success of deep learning in a wide variety of applications has served as a benchmark for the many downstream tasks in AI. The field of computer vision has taken great leaps in recent years and surpassed humans in tasks related to detecting and labeling objects thanks to advances in deep learning and neural networks. Deep Learning Research Applications for Natural Language Processing explains the concepts and state-of-the-art research in the fields of NLP, speech, and computer vision. It provides insights into using the tools and libraries in Python for real-world applications. Covering topics such as deep learning algorithms, neural networks, and advanced prediction, this premier reference source is an excellent resource for computational linguists, software engineers, IT managers, computer scientists, students and faculty of higher education, libraries, researchers, and academicians.
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Deep Learning Applications in Operations Research
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
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First Edition
Condition: New. 2026. 1st Edition. hardcover. . . . . .
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Deep Learning Applications in Operations Research
Language: English
Published by Taylor and Francis Ltd, GB, 2026
ISBN 10: 1032709189 ISBN 13: 9781032709185
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
Hardback. Condition: New. Deep Learning Applications in Operations Research explores cutting-edge applications of deep learning and optimization techniques across various domains. By exploring innovative approaches and emerging trends in advanced intelligent applications, the book examines how innovation and emerging technologies can be leveraged to drive intelligent solutions across diverse domains. It covers such key areas as follows:A comparative study of deep learning algorithms and genetic algorithms as stochastic optimizers, analyzing their effectiveness in operations research applicationsAn updated approach to the critical path method (CPM) that combines traditional scheduling with modern computational methods for dynamic project environmentsA bibliometric analysis of smart warehousing trends in logistics operations management using R, providing data- driven insights into industry developmentsAn examination of edge computing optimization for real-time decision-making in operations research, focusing on latency reduction and computational efficiencyDevelopment of a hybrid intrusion detection system for IoT networks, combining machine learning with anomaly and signature-based detection approachesIntroduction of SAI-GAN, a novel approach for masked face reconstruction, paired with a DCNN-ELM classifier for enhanced biometric authenticationAnalysis of deep learning-driven mHealth applications in India's healthcare system, demonstrating how predictive analytics and real-time monitoring can improve healthcare accessibilityExploration of machine learning-driven ontology evolution in multi-tenant cloud architectures, advancing automated knowledge engineering through deep learning modelsProviding a wide-ranging overview of the field, the book helps researchers navigate the rapidly evolving landscape of advanced intelligent applications. It demonstrates the transformative impact of deep learning on operations research by offering practical insights and establishing a foundation for future innovations.
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Deep Learning Applications in Operations Research
Language: English
Published by Auerbach Publications, 2024
ISBN 10: 1032708026 ISBN 13: 9781032708027
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
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Deep Learning Applications in Operations Research (Advances in Computational Collective Intelligence)
Language: English
Published by Auerbach Publications, 2024
ISBN 10: 1032708026 ISBN 13: 9781032708027
Condition: New.
