Supervised Sequence Labelling Recurrent by Graves Alex (31 results)

hardcover. Condition: Very Good.

Condition: New. A brand new book in pristine condition. Showing zero signs of shelf wear, creases, or damage.

Condition: New. A brand new book in pristine condition. Showing zero signs of shelf wear, creases, or damage.

Paperback. Condition: Good. Supervised Sequence Labelling with Recurrent Neural Networks by Alex GravesStudies in Computational Intelligence, Volume 385Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools-robust to input noise and distortion, able to exploit long-range contextual information-that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary. The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video. Experimental validation is provided by state-of-the-art results in speech and handwriting recognition. In good condition throughout. Copyright © Springer.…

paperback. Condition: Sehr gut. 160 Seiten; 9783642432187.2 Gewicht in Gramm: 500.

Condition: New. In.

Condition: New. In.

Condition: New.

Taschenbuch. Condition: Neu. Supervised Sequence Labelling with Recurrent Neural Networks | Alex Graves | Taschenbuch | Studies in Computational Intelligence | xiv | Englisch | 2014 | Springer | EAN 9783642432187 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools-robust to input noise and distortion, able to exploit long-range contextual information-that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary. The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video. Experimental validation is provided by state-of-the-art results in speech and handwriting recognition.…

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools-robust to input noise and distortion, able to exploit long-range contextual information-that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary. The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video. Experimental validation is provided by state-of-the-art results in speech and handwriting recognition.…

Condition: New. pp. xiv + 146.

Condition: New. pp. 160.

Condition: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools¿robust to input noise and distortion, able to exploit long-range contextual information¿that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary.    The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video.   Experimental validation is provided by state-of-the-art results in speech and handwriting recognition.…

Paperback. Condition: Brand New. 2012 edition. 160 pages. 9.25x6.10x0.47 inches. In Stock.

Hardcover. Condition: Brand New. 2012 edition. 160 pages. 9.50x6.50x0.75 inches. In Stock.

Hardcover. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Paperback. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Condition: gut. 2014. Supervised Sequence Labelling with Recurrent Neural Networks (Studies in Computational Intelligence, Band 385) In deutscher Sprache. pages.

Condition: new. Questo è un articolo print on demand.

Condition: new. Questo è un articolo print on demand.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Recent research in Supervised Sequence Labelling with Recurrent Neural Networks New results in a hot topic Written by leading expertsSupervised sequence labelling is a vital area of machine learning, encompassing tasks such as sp.…

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Recent research in Supervised Sequence Labelling with Recurrent Neural Networks New results in a hot topic Written by leading expertsSupervised sequence labelling is a vital area of machine learning, encompassing tasks such as sp.…

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools-robust to input noise and distortion, able to exploit long-range contextual information-that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary. The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video. Experimental validation is provided by state-of-the-art results in speech and handwriting recognition. 160 pp. Englisch.…

Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools-robust to input noise and distortion, able to exploit long-range contextual information-that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary. The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video. Experimental validation is provided by state-of-the-art results in speech and handwriting recognition. 160 pp. Englisch. …

Condition: New. Print on Demand pp. xiv + 146 62 Illus. (12 Col.).

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools¿robust to input noise and distortion, able to exploit long-range contextual information¿that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary.The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal.Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video.Experimental validation is provided by state-of-the-art results in speech and handwriting recognition.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 160 pp. Englisch. …

Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools¿robust to input noise and distortion, able to exploit long-range contextual information¿that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary.The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal.Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video.Experimental validation is provided by state-of-the-art results in speech and handwriting recognition.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 160 pp. Englisch. …

Condition: New. Print on Demand pp. 160 62 Illus. (12 Col.).

Condition: New. PRINT ON DEMAND pp. xiv + 146.