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Deep Learning Techniques for Biomedical and Health Informatics by
Publication Date: 2020-01-14
Deep Learning Techniques for Biomedical and Health Informatics provides readers with the state-of-the-art in deep learning-based methods for biomedical and health informatics. The book covers not only the best-performing methods, it also presents implementation methods.
An Introduction to Interdisciplinary Toxicology by
Publication Date: 2020-02-18
An Introduction to Interdisciplinary Toxicology: From Molecules to Man integrates the various aspects of toxicology, from "simple" molecular systems, to complex human communities, with expertise from a spectrum of interacting disciplines.
Modeling and Simulation Support for System of Systems Engineering Applications by
Publication Date: 2015-01-09
"...a much-needed handbook with contributions from well-chosen practitioners. A primary accomplishment is to provide guidance for those involved in modeling and simulation in support of Systems of Systems development, more particularly guidance that draws on well-conceived academic research to define concepts and terms, that identifies primary challenges for developers, and that suggests fruitful approaches grounded in theory and successful examples."
Engineering Principles of Combat Modeling and Distributed Simulation by
Publication Date: 2012-02-23
Explore the military and combat applications of modeling andsimulation Engineering Principles of Combat Modeling and DistributedSimulation is the first book of its kind to address the threeperspectives that simulation engineers must master forsuccessful military and defense related modeling: the operationalview (what needs to be modeled); the conceptual view (how to docombat modeling); and the technical view (how to conductdistributed simulation).
Mathematics of Autonomy by
Publication Date: 2017-10-01
Mathematics of Autonomy provides solid mathematical foundations for building useful Autonomous Systems. It clarifies what makes a system autonomous rather than simply automated, and reveals the inherent limitations of systems currently incorrectly labeled as autonomous in reference to the specific and strong uncertainty that characterizes the environments they operate in. Such complex real-world environments demand truly autonomous solutions to provide the flexibility and robustness needed to operate well within them.This volume embraces hybrid solutions to demonstrate extending the classes of uncertainty autonomous systems can handle. In particular, it combines physical-autonomy (robots), cyber-autonomy (agents) and cognitive-autonomy (cyber and embodied cognition) to produce a rigorous subset of trusted autonomy: Cyber-Physical-Cognitive autonomy (CPC-autonomy).The body of the book alternates between underlying theory and applications of CPC-autonomy including 'Autonomous Supervision of a Swarm of Robots', 'Using Wind Turbulence against a Swarm of UAVs' and 'Unique Super-Dynamics for All Kinds of Robots (UAVs, UGVs, UUVs and USVs)' to illustrate how to effectively construct Autonomous Systems using this model. It avoids the wishful thinking that characterizes much discussion related to autonomy, discussing the hard limits and challenges of real autonomous systems. In so doing, it clarifies where more work is needed, and also provides a rigorous set of tools to tackle some of the problem space.
Computational Methods in Synthetic Biology by
Publication Date: 2014-12-09
This volume provides complete coverage of the computational approaches currently used in Synthetic Biology. Chapters focus on computational methods and algorithms for the design of bio-components, insight on CAD programs, analysis techniques, and distributed systems. Written in the highly successful Methods in Molecular Biology series format, the chapters include the kind of detailed description and implementation advice that is crucial for getting optimal results in the laboratory. Authoritative and practical, Computational Methods in Synthetic Biology serves as a guide to plan in silico the in vivo or in vitro construction of a variety of synthetic bio-circuits.
Molecular Devices by
Publication Date: 2018-08-07
Comprehensive look at mechanical molecular devices that mimic the behavior of man-made devices Molecular devices and molecular machines are individual molecules and molecular systems capable of providing valuable device-like functions. Many of them have distinct conventional prototypes and therefore can be identified as technomimetic molecules. The last decade has seen an increasing rate of practical applications of molecular devices and machines, primarily in biomedical and material science fields. Molecular devices: An Introduction to Technomimetics and its Biological Applications focuses on mechanical molecular devices, including the early set of technomimetic molecules. Topics covered include the many simple molecular devices such as container compounds, gearing systems, belts and tubes, and tweezers. It touches upon each molecular machine and discusses in great detail the importance of their applications as well as the latest progress in the fields of chemistry, physics, and biotechnology. Interdisciplinary: Must-have content for physicists, chemists, and biologists Comprehensive: Details an extensive set of mechanical technomimetic molecular devices Thorough: Starts with the fundamental material characterization and finishes with real-world device application Molecular devices: An Introduction to Technomimetics and its Biological Applications is an important book for graduate students, researchers, scientists, and engineers in the fields of chemistry, materials science, molecular physics, engineering, biotechnology, and molecular medicine.
DEEP LEARNING WITH R by
Publication Date: 2018-02-09
Summary Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples. Continue your journey into the world of deep learning with Deep Learning with R in Motion, a practical, hands-on video course available exclusively at Manning.com (www.manning.com/livevideo/deep-learning-with-r-in-motion). Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. Deep-learning systems now enable previously impossible smart applications, revolutionizing image recognition and natural-language processing, and identifying complex patterns in data. The Keras deep-learning library provides data scientists and developers working in R a state-of-the-art toolset for tackling deep-learning tasks. About the Book Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. Initially written for Python as Deep Learning with Python by Keras creator and Google AI researcher François Chollet and adapted for R by RStudio founder J. J. Allaire, this book builds your understanding of deep learning through intuitive explanations and practical examples. You'll practice your new skills with R-based applications in computer vision, natural-language processing, and generative models. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image classification and generation Deep learning for text and sequences About the Reader You'll need intermediate R programming skills. No previous experience with machine learning or deep learning is assumed. About the Authors François Chollet is a deep-learning researcher at Google and the author of the Keras library. J.J. Allaire is the founder of RStudio and the author of the R interfaces to TensorFlow and Keras. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions