Deep Learning

Siddhartha Bhattacharyya

ISBN 10: 3110670798 / ISBN 13: 9783110670790

Edité par De Gruyter, Mercury Learning And Information Jun 2020, 2020

A propos de cet article

This item is printed on demand - Print on Demand Titel. Neuware -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.Walter de Gruyter GmbH, Genthiner Strasse 13, 10785 Berlin 170 pp. Englisch.

N° de réf. du vendeur 9783110670790

Détails bibliographiques

• Titre: Deep Learning
• Éditeur: De Gruyter, Mercury Learning And Information Jun 2020
• Date de parution: 2020
• Langue: anglais
• ISBN à 10 chiffres: 3110670798
• ISBN à 13 chiffres: 9783110670790
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 246 L x 175 H x 17 P (en millimètres) 246 millimètres de largeur par 175 millimètres de hauteur par 17 millimètres de profondeur
• Poids: 513 grammes

A propos de ce titre

Synopsis

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.

À propos de l?auteur

Siddhartha Bhattacharyya, Satadal Saha, B. K. Tripathy, India. Vaclav Snasel, Czech Republic. Aboul Ella Hassanien, Egypt.