http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Neural Network Architectures with Python and Keras
1 online resource (XXI, 308 pages 151 illustrations, 115 illustrations in color.)
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: Examine deep learning code and concepts to apply guiding principles 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.
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.