
Computer Vision Using Deep Learning(sách keo gáy bìa mềm)
Categories:Computers - Computer Science
Year:2021
Edition:1
Publisher:Apress
Language:english
Pages:320
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.