國立高雄大學圖資館 |

登入

回首頁 到查詢結果 [ subject:"Deep learning (Machine learning)" ]

Modern deep learning design and application developmentversatile tools to solve deep learning problems /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modern deep learning design and application developmentby Andre Ye.
其他題名:	versatile tools to solve deep learning problems /
作者:	Ye, Andre.
出版者:	Berkeley, CA :Apress :2022.
面頁冊數:	xix, 451 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Deep learning (Machine learning)
電子資源:	https://doi.org/10.1007/978-1-4842-7413-2
ISBN:	9781484274132$q(electronic bk.)

Modern deep learning design and application developmentversatile 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. - Berkeley, CA :Apress :2022. - xix, 451 p. :ill., digital ;24 cm.

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$q(electronic bk.)

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

913129
Deep learning (Machine learning)

LC Class. No.: Q325.73 / .Y42 2022

Dewey Class. No.: 006.31

Modern deep learning design and application developmentversatile tools to solve deep learning problems /
LDR:03182nmm a2200325 a 4500 001 619874
003 DE-He213
005 20220124160537.0
006 m d
007 cr nn 008maaau
008 221103s2022 cau s 0 eng d
020 $a 9781484274132$q(electronic bk.)
020 $a 9781484274125$q(paper)
024 7 $a 10.1007/978-1-4842-7413-2 $2 doi
035 $a 978-1-4842-7413-2
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.73 $b .Y42 2022
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
090 $a Q325.73 $b .Y37 2022
100 1 $a Ye, Andre. $3 920425
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.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2022.
300 $a xix, 451 p. : $b ill., digital ; $c 24 cm.
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 Deep learning (Machine learning) $3 913129
650 1 4 $a Machine Learning. $3 833608
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Python. $3 763308
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-7413-2
950 $a Professional and Applied Computing (SpringerNature-12059)