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Gursakal, Necmi.

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Synthetic data for deep learning = generate synthetic data for decision making and applications with Python and R /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Synthetic data for deep learning/ by Necmi Gursakal, Sadullah Celik, Esma Birisci.
Reminder of title:	generate synthetic data for decision making and applications with Python and R /
Author:	Gursakal, Necmi.
other author:	Celik, Sadullah.
Published:	Berkeley, CA :Apress : : 2022.,
Description:	xix, 220 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-1-4842-8587-9
ISBN:	9781484285879

Synthetic data for deep learning = generate 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

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

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Synthetic data for deep learning = generate synthetic data for decision making and applications with Python and R /
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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.
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