Details

Computer Vision Using Deep Learning

Neural Network Architectures with Python and Keras

von: Vaibhav Verdhan
56,99 €
Verlag:	Apress
Format:	PDF
Veröffentl.:	15.02.2021
ISBN/EAN:	9781484266168
Sprache:	englisch

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Als Gutschein

Dieses eBook enthält ein Wasserzeichen.

Beschreibungen

Titelbeschreibung

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

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.

Autorenportrait

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.

Back cover copy

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. You will: <ul><li>Examine deep learning code and concepts to apply guiding principles to your own projects</li><li>Classify and evaluate various architectures to better understand your options in various use cases</li><li>Go behind the scenes of basic deep learning functions to find out how they work</li></ul>

Sonderbeitrag

Implement Deep Learning solutions on your own systems to bridge the gap between theory and practiceExamine the inner workings of the codes and libraries that make Deep Learning applications workCreate solutions for computer vision design using Keras and TensorFlow