introduction deep learning using (44 Ergebnisse)

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a short introduction and easy-to-follow implementation steps of deep learning using Google Cloud Platform. It also includes a practical case study that highlights the utilization of Python and related libraries for running a pre-trained deep learning model.In recent years, deep learning-based modeling approaches have been used in a wide variety of engineering domains, such as autonomous cars, intelligent robotics, computer vision, natural language processing, and bioinformatics. Also, numerous real-world engineering applications utilize an existing pre-trained deep learning model that has already been developed and optimized for a related task. However, incorporating a deep learning model in a research project is quite challenging, especially for someone who doesn't have related machine learning and cloud computing knowledge. Keeping that in mind, this book is intended to be a short introduction of deep learning basics through the example of a practical implementation case.The audience of this short book is undergraduate engineering students who wish to explore deep learning models in their class project or senior design project without having a full journey through the machine learning theories. The case study part at the end also provides a cost-effective and step-by-step approach that can be replicated by others easily.

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Taschenbuch. Zustand: Neu. Introduction to Deep Learning for Engineers | Using Python and Google Cloud Platform | Tariq M. Arif | Taschenbuch | xv | Englisch | 2020 | Springer International Publishing | EAN 9783031796647 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

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Taschenbuch. Zustand: Neu. Neuware -Understand deep learning, the nuances of its different models, and where these models can be applied.The abundance of data and demand for superior products/services have driven the development of advanced computer science techniques, among them image and speech recognition. Introduction to Deep Learning Using R provides a theoretical and practical understanding of the models that perform these tasks by building upon the fundamentals of data science through machine learning and deep learning. This step-by-step guide will help you understand the disciplines so that you can apply the methodology in a variety of contexts. All examples are taught in the R statistical language, allowing students and professionals to implement these techniques using open source tools.What You'll LearnUnderstand the intuition and mathematics that power deep learning modelsUtilize various algorithms using the R programming language and its packagesUse best practices for experimental design and variable selectionPractice the methodology to approach and effectively solve problems as a data scientistEvaluate the effectiveness of algorithmic solutions and enhance their predictive powerAPress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 252 pp. Englisch.

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Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 272 | Sprache: Englisch | Produktart: Bücher | 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.

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

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