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.
