Deep Learning Foundations [electronic resource] / by Taeho Jo.

Por: Jo, Taeho [author.]Colaborador(es): SpringerLink (Online service)Tipo de material: TextoTextoEditor: Cham : Springer International Publishing : Imprint: Springer, 2023Edición: 1st ed. 2023Descripción: XX, 426 p. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783031328794Tema(s): Telecommunication | Machine learning | Computational intelligence | Pattern recognition systems | Neural networks (Computer science)  | Communications Engineering, Networks | Machine Learning | Computational Intelligence | Automated Pattern Recognition | Mathematical Models of Cognitive Processes and Neural NetworksFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 621.382 Clasificación LoC:TK5101-5105.9Recursos en línea: Libro electrónicoTexto
Contenidos:
Introduction -- Part I. Foundation -- Supervised Learning -- Unsupervised Learning -- Ensemble Learning -- Part II. Deep Machine Learning -- Deep K Nearest Neighbor -- Deep Probabilistic Learning -- Deep Decision Tree -- Deep SVM -- Part III. Deep Neural Networks -- Multiple Layer Perceptron -- Recurrent Networks -- Restricted Boltzmann Machine -- Convolutionary Neural Networks -- Part IV. Textual Deep Learning -- Index Expansion -- Text Summarization -- Textual Deep Operations -- Convolutionary Text Classifier -- Conclusion.
En: Springer Nature eBookResumen: This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms into deep learning algorithms. The book's third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning. Provides a conceptual understanding of deep learning algorithms; Presents ways of modifying existing machine learning algorithms into deep learning algorithms for further analysis; Details how deep learning can solve problems such as classification, regression, and clustering. .
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Acceso multiusuario

Introduction -- Part I. Foundation -- Supervised Learning -- Unsupervised Learning -- Ensemble Learning -- Part II. Deep Machine Learning -- Deep K Nearest Neighbor -- Deep Probabilistic Learning -- Deep Decision Tree -- Deep SVM -- Part III. Deep Neural Networks -- Multiple Layer Perceptron -- Recurrent Networks -- Restricted Boltzmann Machine -- Convolutionary Neural Networks -- Part IV. Textual Deep Learning -- Index Expansion -- Text Summarization -- Textual Deep Operations -- Convolutionary Text Classifier -- Conclusion.

This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms into deep learning algorithms. The book's third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning. Provides a conceptual understanding of deep learning algorithms; Presents ways of modifying existing machine learning algorithms into deep learning algorithms for further analysis; Details how deep learning can solve problems such as classification, regression, and clustering. .

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