Deep learning for data analytics : foundations, biomedical applications, and challenges /

Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges prov...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Das, Himansu, Pradhan, Chittaranjan, Dey, Nilanjan, 1984-
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Academic Press, 2020.
Temas:
Machine learning.
Apprentissage automatique.
Machine learning
Acceso en línea:Texto completo

MARC

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245 0 0 |a Deep learning for data analytics :  |b foundations, biomedical applications, and challenges /  |c edited by Himansu Das, Chittaranjan Pradhan and Nilanjan Dey. 
264 1 |a London :  |b Academic Press,  |c 2020. 
300 |a 1 online resource 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
504 |a Includes bibliographical references and index. 
520 |a Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear processing, which aids in feature extraction and learning in supervised and unsupervised ways, including classification and pattern analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning algorithms extract high level complex data and process these complex sets to relatively simpler ideas formulated in the preceding level of the hierarchy. The authors of this book focus on suitable data analytics methods to solve complex real world problems such as medical image recognition, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinoformatics, audio recognition, drug design, and medical image analysis.--  |c Provided by publisher. 
650 0 |a Machine learning. 
650 6 |a Apprentissage automatique.  |0 (CaQQLa)201-0131435 
650 7 |a Machine learning  |2 fast  |0 (OCoLC)fst01004795 
700 1 |a Das, Himansu. 
700 1 |a Pradhan, Chittaranjan. 
700 1 |a Dey, Nilanjan,  |d 1984- 
776 0 8 |i Print version:  |z 0128197641  |z 9780128197646  |w (OCoLC)1130899746 
856 4 0 |u https://sciencedirect.uam.elogim.com/science/book/9780128197646  |z Texto completo 

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