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Computer Vision Using Deep Learning - Neural Network Architectures with Python and Keras (Paperback, 1st ed.)
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Computer Vision Using Deep Learning - Neural Network Architectures with Python and Keras (Paperback, 1st ed.)
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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.
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