Computer Vision Using Deep Learning
Apress (Verlag)
978-1-4842-6615-1 (ISBN)
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.
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.
Chapter 1Introduction to Computer Vision and Deep Learning.- Chapter 2Nuts and Bolts of Deep Learning for Computer Vision.- Chapter 3Image Classification using LeNet.- Chapter 4 VGGNet and AlexNext Networks.- Chapter 5Object Detection Using Deep Learning.- Chapter 6Facial Recognition and Gesture Recognition.- Chapter 7 Video Analytics Using Deep Learning.- Chapter 8End-to-end Model Development.- Appendix.
Erscheinungsdatum | 24.02.2021 |
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Zusatzinfo | 115 Illustrations, color; 36 Illustrations, black and white; XXI, 308 p. 151 illus., 115 illus. in color. |
Verlagsort | Berkley |
Sprache | englisch |
Maße | 155 x 235 mm |
Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
Schlagworte | AI • Artificial Intelligence • computer vision • Deep learning • Facial Recognition • image classification • NN architecture • Object detection • pose estimation |
ISBN-10 | 1-4842-6615-3 / 1484266153 |
ISBN-13 | 978-1-4842-6615-1 / 9781484266151 |
Zustand | Neuware |
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