國立高雄大學圖資館 |

登入

回首頁

Synthetic data for deep learninggenerate synthetic data for decision making and applications with Python and R /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Synthetic data for deep learningby Necmi Gursakal, Sadullah Celik, Esma Birisci.
其他題名:	generate synthetic data for decision making and applications with Python and R /
作者:	Gursakal, Necmi.
其他作者:	Celik, Sadullah.
出版者:	Berkeley, CA :Apress :2022.
面頁冊數:	xix, 220 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Machine learning.
電子資源:	https://doi.org/10.1007/978-1-4842-8587-9
ISBN:	9781484285879$q(electronic bk.)

Synthetic data for deep learninggenerate synthetic data for decision making and applications with Python and R /
Gursakal, Necmi.

Synthetic data for deep learninggenerate synthetic data for decision making and applications with Python and R /[electronic resource] :by Necmi Gursakal, Sadullah Celik, Esma Birisci. - Berkeley, CA :Apress :2022. - xix, 220 p. :ill., digital ;24 cm.

Chapter I: Introduction to Data -- Chapter 2: Synthetic Data -- Chapter 3: Synthetic Data Generation with R -- Chapter 4: GANs -- Chapter 5: Synthetic Data Generation with Python.

Data is the indispensable fuel that drives the decision making of everything from governments, to major corporations, to sports teams. Its value is almost beyond measure. But what if that data is either unavailable or problematic to access? That's where synthetic data comes in. This book will show you how to generate synthetic data and use it to maximum effect. Synthetic Data for Deep Learning begins by tracing the need for and development of synthetic data before delving into the role it plays in machine learning and computer vision. You'll gain insight into how synthetic data can be used to study the benefits of autonomous driving systems and to make accurate predictions about real-world data. You'll work through practical examples of synthetic data generation using Python and R, placing its purpose and methods in a real-world context. Generative Adversarial Networks (GANs) are also covered in detail, explaining how they work and their potential applications. After completing this book, you'll have the knowledge necessary to generate and use synthetic data to enhance your corporate, scientific, or governmental decision making. What You Will Learn Create synthetic tabular data with R and Python Understand how synthetic data is important for artificial neural networks Master the benefits and challenges of synthetic data Understand concepts such as domain randomization and domain adaptation related to synthetic data generation.

ISBN: 9781484285879$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-8587-9doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Synthetic data for deep learninggenerate synthetic data for decision making and applications with Python and R /
LDR:02705nmm a2200325 a 4500 001 634258
003 DE-He213
005 20221216163724.0
006 m d
007 cr nn 008maaau
008 230511s2022 cau s 0 eng d
020 $a 9781484285879$q(electronic bk.)
020 $a 9781484285862$q(paper)
024 7 $a 10.1007/978-1-4842-8587-9 $2 doi
035 $a 978-1-4842-8587-9
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .G981 2022
100 1 $a Gursakal, Necmi. $3 940568
245 1 0 $a Synthetic data for deep learning $h [electronic resource] : $b generate synthetic data for decision making and applications with Python and R / $c by Necmi Gursakal, Sadullah Celik, Esma Birisci.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2022.
300 $a xix, 220 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter I: Introduction to Data -- Chapter 2: Synthetic Data -- Chapter 3: Synthetic Data Generation with R -- Chapter 4: GANs -- Chapter 5: Synthetic Data Generation with Python.
520 $a Data is the indispensable fuel that drives the decision making of everything from governments, to major corporations, to sports teams. Its value is almost beyond measure. But what if that data is either unavailable or problematic to access? That's where synthetic data comes in. This book will show you how to generate synthetic data and use it to maximum effect. Synthetic Data for Deep Learning begins by tracing the need for and development of synthetic data before delving into the role it plays in machine learning and computer vision. You'll gain insight into how synthetic data can be used to study the benefits of autonomous driving systems and to make accurate predictions about real-world data. You'll work through practical examples of synthetic data generation using Python and R, placing its purpose and methods in a real-world context. Generative Adversarial Networks (GANs) are also covered in detail, explaining how they work and their potential applications. After completing this book, you'll have the knowledge necessary to generate and use synthetic data to enhance your corporate, scientific, or governmental decision making. What You Will Learn Create synthetic tabular data with R and Python Understand how synthetic data is important for artificial neural networks Master the benefits and challenges of synthetic data Understand concepts such as domain randomization and domain adaptation related to synthetic data generation.
650 0 $a Machine learning. $3 188639
650 0 $a Computer vision. $3 200113
700 1 $a Celik, Sadullah. $3 940569
700 1 $a Birisci, Esma. $3 940570
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-8587-9
950 $a Professional and Applied Computing (SpringerNature-12059)