Computer Vision Using Deep Leaing: Neural Network Architectures with Python and Keras

by: Vaibhav Verdhan (Author)

Publisher:

Edition: 1st ed.

Publication Date: 2021/2/15

Language: English

Print Length: 332 pages

ISBN-10: 1484266153

ISBN-13: 9781484266151

Book Description

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 leaing 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 Leaing 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 LeaExamine deep leaing code and concepts to apply guiding principals to your own projectsClassify and evaluate various architectures to better understand your options in various use casesGo behind the scenes of basic deep leaing functions to find out how they workWho This Book Is ForProfessional 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 Leaing.

About the Author

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 leaing 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 Leaing 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 LeaExamine deep leaing code and concepts to apply guiding principals to your own projectsClassify and evaluate various architectures to better understand your options in various use casesGo behind the scenes of basic deep leaing functions to find out how they workWho This Book Is ForProfessional 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 Leaing.