國立高雄大學圖資館 |

Language: English

Back

Introduction to deep learning busine...

Ribeiro, Bernardete.

Introduction to deep learning business applications for developersfrom conversational bots in customer service to medical image processing /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Introduction to deep learning business applications for developersby Armando Vieira, Bernardete Ribeiro.
Reminder of title:	from conversational bots in customer service to medical image processing /
Author:	Vieira, Armando.
other author:	Ribeiro, Bernardete.
Published:	Berkeley, CA :Apress :2018.
Description:	xxi, 343 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learning.
Online resource:	http://dx.doi.org/10.1007/978-1-4842-3453-2
ISBN:	9781484234532$q(electronic bk.)

Introduction to deep learning business applications for developersfrom conversational bots in customer service to medical image processing /
Vieira, Armando.

Introduction to deep learning business applications for developersfrom conversational bots in customer service to medical image processing /[electronic resource] :by Armando Vieira, Bernardete Ribeiro. - Berkeley, CA :Apress :2018. - xxi, 343 p. :ill., digital ;24 cm.

1 Introduction -- 2 Deep Learning: An Overview -- 3 Deep Neural Network Models -- 4 Image Processing -- 5 Natural Language Processing and Speech -- 6 Reinforcement Learning and Robotics -- 7 Recommendations Algorithms and Advertising -- 8 Games and Art -- 9 Other Applications -- 10 Business Impact of DL Technology -- 11 New Research and Future Directions -- Appendix Training DNN with Keras.

Discover the potential applications, challenges, and opportunities of deep learning from a business perspective with technical examples. These applications include image recognition, segmentation and annotation, video processing and annotation, voice recognition, intelligent personal assistants, automated translation, and autonomous vehicles. An Introduction to Deep Learning Business Applications for Developers covers some common DL algorithms such as content-based recommendation algorithms and natural language processing. You'll explore examples, such as video prediction with fully convolutional neural networks (FCNN) and residual neural networks (ResNets) You will also see applications of DL for controlling robotics, exploring the DeepQ learning algorithm with Monte Carlo Tree search (used to beat humans in the game of Go), and modeling for financial risk assessment. There will also be mention of the powerful set of algorithms called Generative Adversarial Neural networks (GANs) that can be applied for image colorization, image completion, and style transfer. After reading this book you will have an overview of the exciting field of deep neural networks and an understanding of most of the major applications of deep learning. The book contains some coding examples, tricks, and insights on how to train deep learning models using the Keras framework. You will: Find out about deep learning and why it is so powerful Work with the major algorithms available to train deep learning models See the major breakthroughs in terms of applications of deep learning Run simple examples with a selection of deep learning libraries Discover the areas of impact of deep learning in business.

ISBN: 9781484234532$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-3453-2doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Introduction to deep learning business applications for developersfrom conversational bots in customer service to medical image processing /
LDR:03185nmm a2200325 a 4500 001 538015
003 DE-He213
005 20180502121313.0
006 m d
007 cr nn 008maaau
008 190116s2018 cau s 0 eng d
020 $a 9781484234532$q(electronic bk.)
020 $a 9781484234525$q(paper)
024 7 $a 10.1007/978-1-4842-3453-2 $2 doi
035 $a 978-1-4842-3453-2
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UMA $2 bicssc
072 7 $a COM014000 $2 bisacsh
072 7 $a COM018000 $2 bisacsh
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .V658 2018
100 1 $a Vieira, Armando. $3 815283
245 1 0 $a Introduction to deep learning business applications for developers $h [electronic resource] : $b from conversational bots in customer service to medical image processing / $c by Armando Vieira, Bernardete Ribeiro.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2018.
300 $a xxi, 343 p. : $b ill., digital ; $c 24 cm.
505 0 $a 1 Introduction -- 2 Deep Learning: An Overview -- 3 Deep Neural Network Models -- 4 Image Processing -- 5 Natural Language Processing and Speech -- 6 Reinforcement Learning and Robotics -- 7 Recommendations Algorithms and Advertising -- 8 Games and Art -- 9 Other Applications -- 10 Business Impact of DL Technology -- 11 New Research and Future Directions -- Appendix Training DNN with Keras.
520 $a Discover the potential applications, challenges, and opportunities of deep learning from a business perspective with technical examples. These applications include image recognition, segmentation and annotation, video processing and annotation, voice recognition, intelligent personal assistants, automated translation, and autonomous vehicles. An Introduction to Deep Learning Business Applications for Developers covers some common DL algorithms such as content-based recommendation algorithms and natural language processing. You'll explore examples, such as video prediction with fully convolutional neural networks (FCNN) and residual neural networks (ResNets) You will also see applications of DL for controlling robotics, exploring the DeepQ learning algorithm with Monte Carlo Tree search (used to beat humans in the game of Go), and modeling for financial risk assessment. There will also be mention of the powerful set of algorithms called Generative Adversarial Neural networks (GANs) that can be applied for image colorization, image completion, and style transfer. After reading this book you will have an overview of the exciting field of deep neural networks and an understanding of most of the major applications of deep learning. The book contains some coding examples, tricks, and insights on how to train deep learning models using the Keras framework. You will: Find out about deep learning and why it is so powerful Work with the major algorithms available to train deep learning models See the major breakthroughs in terms of applications of deep learning Run simple examples with a selection of deep learning libraries Discover the areas of impact of deep learning in business.
650 0 $a Machine learning. $3 188639
650 0 $a Application software $x Development. $3 189413
650 0 $a Computer science. $3 199325
650 0 $a Computers. $3 202174
650 1 4 $a Computer Science. $3 212513
650 2 4 $a Computing Methodologies. $3 274528
650 2 4 $a Python. $3 763308
700 1 $a Ribeiro, Bernardete. $3 256005
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-1-4842-3453-2
950 $a Professional and Applied Computing (Springer-12059)