Applied deep learning : a case-based approach to understanding deep neural networks

Michelucci, Umberto

doi:10.1007/978-1-4842-3790-8

Publication type:	Book
Type of review:	Editorial review
Title:	Applied deep learning : a case-based approach to understanding deep neural networks
Authors:	Michelucci, Umberto
DOI:	10.1007/978-1-4842-3790-8
Extent:	450
Issue Date:	2018
Edition:	1. Auflage
Publisher / Ed. Institution:	Apress
Publisher / Ed. Institution:	New York
ISBN:	978-1-4842-3789-2 978-1-4842-3790-8
Language:	English
Subjects:	Deep Learning; Machine Learning; Python; TensorFlow; Neural Networks; Keras
Subject (DDC):	006: Special computer methods
URI:	https://digitalcollection.zhaw.ch/handle/11475/16484
Fulltext version:	Published version
License (according to publishing contract):	Licence according to publishing contract
Departement:	Life Sciences and Facility Management
Organisational Unit:	Institute of Computational Life Sciences (ICLS)
Appears in collections:	Publikationen Life Sciences und Facility Management

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Michelucci, U. (2018). Applied deep learning : a case-based approach to understanding deep neural networks (1. Auflage). Apress. https://doi.org/10.1007/978-1-4842-3790-8

Michelucci, U. (2018) Applied deep learning : a case-based approach to understanding deep neural networks. 1. Auflage. New York: Apress. Available at: https://doi.org/10.1007/978-1-4842-3790-8.

U. Michelucci, Applied deep learning : a case-based approach to understanding deep neural networks, 1. Auflage. New York: Apress, 2018. doi: 10.1007/978-1-4842-3790-8.

MICHELUCCI, Umberto, 2018. Applied deep learning : a case-based approach to understanding deep neural networks. 1. Auflage. New York: Apress. ISBN 978-1-4842-3789-2

Michelucci, Umberto. 2018. Applied Deep Learning : A Case-Based Approach to Understanding Deep Neural Networks. 1. Auflage. New York: Apress. https://doi.org/10.1007/978-1-4842-3790-8.

Michelucci, Umberto. Applied Deep Learning : A Case-Based Approach to Understanding Deep Neural Networks. 1. Auflage, Apress, 2018, https://doi.org/10.1007/978-1-4842-3790-8.