Deep Learning for Vision Systems

Deep Learning for Vision Systems teaches you to apply deep learning techniques to solve real-world computer vision problems. In his straightforward and accessible style, DL and CV expert Mohamed Elgendy introduces you to the concept of visual intuition—how a machine learns to understand what it sees. Then you’ll explore the DL algorithms used in different CV applications. You’ll drill down into the different parts of the CV interpreting system, or pipeline. Using Python, OpenCV, Keras, Tensorflow, and Amazon’s MxNet, you’ll discover advanced DL techniques for solving CV problems.
Applications of focus include image classification, segmentation, captioning, and generation as well as face recognition and analysis. You’ll also cover the most important deep learning architectures including artificial neural networks (ANNs), convolutional networks (CNNs), and recurrent networks (RNNs), knowledge that you can apply to related deep learning disciplines like natural language processing and voice user interface. Real-life, scalable projects from Amazon, Google, and Facebook drive it all home. With this invaluable book, you’ll gain the essential skills for building amazing end-to-end CV projects that solve real-world problems.
What's inside
Introduction to computer vision
Deep learning and neural networks
Transfer learning and advanced CNN architectures
Image classification and captioning
Object detection with YOLO, SSD and R-CNN
Style transfer
AI ethics
Real-world projects