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Deep learning with Python = a hands-...

Ketkar, Nikhil.

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Deep learning with Python = a hands-on introduction /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning with Python/ by Nikhil Ketkar.
Reminder of title:	a hands-on introduction /
Author:	Ketkar, Nikhil.
Published:	Berkeley, CA :Apress : : 2017.,
Description:	xv, 160 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learning. -
Online resource:	http://dx.doi.org/10.1007/978-1-4842-2766-4
ISBN:	9781484227664

Deep learning with Python = a hands-on introduction /
Ketkar, Nikhil.

Deep learning with Pythona hands-on introduction /[electronic resource] :by Nikhil Ketkar. - Berkeley, CA :Apress :2017. - xv, 160 p. :ill., digital ;24 cm.

Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process.Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. You will: Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production.

ISBN: 9781484227664

Standard No.: 10.1007/978-1-4842-2766-4doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: QA76.73.P98

Dewey Class. No.: 005.133

Deep learning with Python = a hands-on introduction /
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520 $a Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process.Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. You will: Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production.
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