國立虎尾科技大學 |

Modern Deep Learning Design and Application Development = Versatile Tools to Solve Deep Learning Problems /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Modern Deep Learning Design and Application Development/ by Andre Ye.
Reminder of title:	Versatile Tools to Solve Deep Learning Problems /
Author:	Ye, Andre.
Description:	XIX, 451 p. 204 illus.online resource. :
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-1-4842-7413-2
ISBN:	9781484274132

Modern Deep Learning Design and Application Development = Versatile Tools to Solve Deep Learning Problems /
Ye, Andre.

Modern Deep Learning Design and Application DevelopmentVersatile Tools to Solve Deep Learning Problems /[electronic resource] :by Andre Ye. - 1st ed. 2022. - XIX, 451 p. 204 illus.online resource.

Chapter 1: A Deep Dive Into Keras -- Chapter 2: Pre-training Strategies and Transfer Learning -- Chapter 3: The Versatility of Autoencoders -- Chapter 4: Model Compression for Practical Deployment -- Chapter 5: Automating Model Design with Meta-Optimization -- Chapter 6:Successful Neural Network Architecture Design -- Chapter 7:Reframing Difficult Deep Learning Problems.

Learn how to harness modern deep-learning methods in many contexts. Packed with intuitive theory, practical implementation methods, and deep-learning case studies, this book reveals how to acquire the tools you need to design and implement like a deep-learning architect. It covers tools deep learning engineers can use in a wide range of fields, from biology to computer vision to business. With nine in-depth case studies, this book will ground you in creative, real-world deep learning thinking. You’ll begin with a structured guide to using Keras, with helpful tips and best practices for making the most of the framework. Next, you’ll learn how to train models effectively with transfer learning and self-supervised pre-training. You will then learn how to use a variety of model compressions for practical usage. Lastly, you will learn how to design successful neural network architectures and creatively reframe difficult problems into solvable ones. You’ll learn not only to understand and apply methods successfully but to think critically about it. Modern Deep Learning Design and Methods is ideal for readers looking to utilize modern, flexible, and creative deep-learning design and methods. Get ready to design and implement innovative deep-learning solutions to today’s difficult problems. You will: Improve the performance of deep learning models by using pre-trained models, extracting rich features, and automating optimization. Compress deep learning models while maintaining performance. Reframe a wide variety of difficult problems and design effective deep learning solutions to solve them. Use the Keras framework, with some help from libraries like HyperOpt, TensorFlow, and PyTorch, to implement a wide variety of deep learning approaches.

ISBN: 9781484274132

Standard No.: 10.1007/978-1-4842-7413-2doiSubjects--Topical Terms:

561253
Machine learning.

LC Class. No.: Q325.5-.7

Dewey Class. No.: 006.31

Modern Deep Learning Design and Application Development = Versatile Tools to Solve Deep Learning Problems /
LDR:03530nam a22003975i 4500 001 1093347
003 DE-He213
005 20220512132403.0
007 cr nn 008mamaa
008 221228s2022 xxu| s |||| 0|eng d
020 $a 9781484274132 $9 978-1-4842-7413-2
024 7 $a 10.1007/978-1-4842-7413-2 $2 doi
035 $a 978-1-4842-7413-2
050 4 $a Q325.5-.7
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 006.31 $2 23
100 1 $a Ye, Andre. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1401270
245 1 0 $a Modern Deep Learning Design and Application Development $h [electronic resource] : $b Versatile Tools to Solve Deep Learning Problems / $c by Andre Ye.
250 $a 1st ed. 2022.
264 1 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2022.
300 $a XIX, 451 p. 204 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a Chapter 1: A Deep Dive Into Keras -- Chapter 2: Pre-training Strategies and Transfer Learning -- Chapter 3: The Versatility of Autoencoders -- Chapter 4: Model Compression for Practical Deployment -- Chapter 5: Automating Model Design with Meta-Optimization -- Chapter 6:Successful Neural Network Architecture Design -- Chapter 7:Reframing Difficult Deep Learning Problems.
520 $a Learn how to harness modern deep-learning methods in many contexts. Packed with intuitive theory, practical implementation methods, and deep-learning case studies, this book reveals how to acquire the tools you need to design and implement like a deep-learning architect. It covers tools deep learning engineers can use in a wide range of fields, from biology to computer vision to business. With nine in-depth case studies, this book will ground you in creative, real-world deep learning thinking. You’ll begin with a structured guide to using Keras, with helpful tips and best practices for making the most of the framework. Next, you’ll learn how to train models effectively with transfer learning and self-supervised pre-training. You will then learn how to use a variety of model compressions for practical usage. Lastly, you will learn how to design successful neural network architectures and creatively reframe difficult problems into solvable ones. You’ll learn not only to understand and apply methods successfully but to think critically about it. Modern Deep Learning Design and Methods is ideal for readers looking to utilize modern, flexible, and creative deep-learning design and methods. Get ready to design and implement innovative deep-learning solutions to today’s difficult problems. You will: Improve the performance of deep learning models by using pre-trained models, extracting rich features, and automating optimization. Compress deep learning models while maintaining performance. Reframe a wide variety of difficult problems and design effective deep learning solutions to solve them. Use the Keras framework, with some help from libraries like HyperOpt, TensorFlow, and PyTorch, to implement a wide variety of deep learning approaches.
650 0 $a Machine learning. $3 561253
650 0 $a Artificial intelligence. $3 559380
650 0 $a Python (Computer program language). $3 1127623
650 1 4 $a Machine Learning. $3 1137723
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Python. $3 1115944
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9781484274125
776 0 8 $i Printed edition: $z 9781484274149
776 0 8 $i Printed edition: $z 9781484284858
856 4 0 $u https://doi.org/10.1007/978-1-4842-7413-2
912 $a ZDB-2-CWD
912 $a ZDB-2-SXPC
950 $a Professional and Applied Computing (SpringerNature-12059)
950 $a Professional and Applied Computing (R0) (SpringerNature-43716)