Dekorationsartikel gehören nicht zum Leistungsumfang.
Computer Vision Using Deep Learning
Neural Network Architectures with Python and Keras
Taschenbuch von Vaibhav Verdhan
Sprache: Englisch

54,40 €*

inkl. MwSt.

Versandkostenfrei per Post / DHL

Lieferzeit 4-7 Werktage

Kategorien:
Beschreibung
Organizations spend huge resources in developing software that can perform the way a human does. Image classification, object detection and tracking, pose estimation, facial recognition, and sentiment estimation all play a major role in solving computer vision problems.
This book will bring into focus these and other deep learning architectures and techniques to help you create solutions using Keras and the TensorFlow library. You'll also review mutliple neural network architectures, including LeNet, AlexNet, VGG, Inception, R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN, YOLO, and SqueezeNet and see how they work alongside Python code via best practices, tips, tricks, shortcuts, and pitfalls. All code snippets will be broken down and discussed thoroughly so you can implement the same principles in your respective environments.

Computer Vision Using Deep Learning offers a comprehensive yet succinct guide that stitches DL and CV together to automate operations, reduce human intervention, increase capability, and cut the costs.

What You'll Learn

Examine deep learning code and concepts to apply guiding principals to your own projects
Classify and evaluate various architectures to better understand your options in various use cases
Go behind the scenes of basic deep learning functions to find out how they work

Who This Book Is For
Professional practitioners working in the fields of software engineering and data science. A working knowledge of Python is strongly recommended. Students and innovators working on advanced degrees in areas related to computer vision and Deep Learning.
Organizations spend huge resources in developing software that can perform the way a human does. Image classification, object detection and tracking, pose estimation, facial recognition, and sentiment estimation all play a major role in solving computer vision problems.
This book will bring into focus these and other deep learning architectures and techniques to help you create solutions using Keras and the TensorFlow library. You'll also review mutliple neural network architectures, including LeNet, AlexNet, VGG, Inception, R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN, YOLO, and SqueezeNet and see how they work alongside Python code via best practices, tips, tricks, shortcuts, and pitfalls. All code snippets will be broken down and discussed thoroughly so you can implement the same principles in your respective environments.

Computer Vision Using Deep Learning offers a comprehensive yet succinct guide that stitches DL and CV together to automate operations, reduce human intervention, increase capability, and cut the costs.

What You'll Learn

Examine deep learning code and concepts to apply guiding principals to your own projects
Classify and evaluate various architectures to better understand your options in various use cases
Go behind the scenes of basic deep learning functions to find out how they work

Who This Book Is For
Professional practitioners working in the fields of software engineering and data science. A working knowledge of Python is strongly recommended. Students and innovators working on advanced degrees in areas related to computer vision and Deep Learning.
Über den Autor

Vaibhav Verdhan is a seasoned data science professional with rich experience spanning across geographies and retail, telecom, manufacturing, health-care and utilities domain. He is a hands-on technical expert and has led multiple engagements in Machine Learning and Artificial Intelligence. He is a leading industry expert, is a regular speaker at conferences and meet-ups and mentors students and professionals. Currently he resides in Ireland and is working as a Principal Data Scientist.

Zusammenfassung

Implement Deep Learning solutions on your own systems to bridge the gap between theory and practice

Examine the inner workings of the codes and libraries that make Deep Learning applications work

Create solutions for computer vision design using Keras and TensorFlow

Inhaltsverzeichnis
Chapter 1 Introduction to Computer Vision and Deep Learning.- Chapter 2 Nuts and Bolts of Deep Learning for Computer Vision.- Chapter 3 Image Classification using LeNet.- Chapter 4 VGGNet and AlexNext Networks.- Chapter 5 Object Detection Using Deep Learning.- Chapter 6 Facial Recognition and Gesture Recognition.- Chapter 7 Video Analytics Using Deep Learning.- Chapter 8 End-to-end Model Development.- Appendix.
Details
Erscheinungsjahr: 2021
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Seiten: 308
Inhalt: xxi
308 S.
36 s/w Illustr.
115 farbige Illustr.
308 p. 151 illus.
115 illus. in color.
ISBN-13: 9781484266151
ISBN-10: 1484266153
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Verdhan, Vaibhav
Hersteller: APRESS
Maße: 235 x 155 x 17 mm
Von/Mit: Vaibhav Verdhan
Erscheinungsdatum: 15.02.2021
Gewicht: 0,505 kg
preigu-id: 119028490
Über den Autor

Vaibhav Verdhan is a seasoned data science professional with rich experience spanning across geographies and retail, telecom, manufacturing, health-care and utilities domain. He is a hands-on technical expert and has led multiple engagements in Machine Learning and Artificial Intelligence. He is a leading industry expert, is a regular speaker at conferences and meet-ups and mentors students and professionals. Currently he resides in Ireland and is working as a Principal Data Scientist.

Zusammenfassung

Implement Deep Learning solutions on your own systems to bridge the gap between theory and practice

Examine the inner workings of the codes and libraries that make Deep Learning applications work

Create solutions for computer vision design using Keras and TensorFlow

Inhaltsverzeichnis
Chapter 1 Introduction to Computer Vision and Deep Learning.- Chapter 2 Nuts and Bolts of Deep Learning for Computer Vision.- Chapter 3 Image Classification using LeNet.- Chapter 4 VGGNet and AlexNext Networks.- Chapter 5 Object Detection Using Deep Learning.- Chapter 6 Facial Recognition and Gesture Recognition.- Chapter 7 Video Analytics Using Deep Learning.- Chapter 8 End-to-end Model Development.- Appendix.
Details
Erscheinungsjahr: 2021
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Seiten: 308
Inhalt: xxi
308 S.
36 s/w Illustr.
115 farbige Illustr.
308 p. 151 illus.
115 illus. in color.
ISBN-13: 9781484266151
ISBN-10: 1484266153
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Verdhan, Vaibhav
Hersteller: APRESS
Maße: 235 x 155 x 17 mm
Von/Mit: Vaibhav Verdhan
Erscheinungsdatum: 15.02.2021
Gewicht: 0,505 kg
preigu-id: 119028490
Warnhinweis

Ähnliche Produkte

Ähnliche Produkte