Deep Learning for Vision Systems
- Paperback: 475 pages
- Publisher: WOW! eBook; 1st edition (June 9, 2020)
- Language: English
- ISBN-10: 1617296198
- ISBN-13: 978-1617296192
Computer vision is central to many leading-edge innovations, including self-driving cars, drones, augmented reality, facial recognition, and much, much more. Amazing new computer vision applications are developed every day, thanks to rapid advances in AI and deep learning (DL). Deep Learning for Vision Systems teaches you the concepts and tools for building intelligent, scalable computer vision systems that can identify and react to objects in images, videos, and real life. With author Mohamed Elgendy’s expert instruction and illustration of real-world projects, you’ll finally grok state-of-the-art deep learning techniques, so you can build, contribute to, and lead in the exciting realm of computer vision!
By using deep neural networks, AI systems make decisions based on their perceptions of their input data. Deep learning-based computer vision (CV) techniques, which enhance and interpret visual perceptions, makes tasks like image recognition, generation, and classification possible. Exciting advances in CV have led to solutions in a wide range of industries including robotics, automation, agriculture, healthcare, and security, just to name a few. In many cases, CV is deemed more accurate than human vision, which is an important distinction when you think about what that means for CV programs that can detect skin cancer or find anomalies in medical diagnostic scans. Whether we’re talking about self-driving cars or life-saving medical programs, there’s no denying that the application of deep learning for computer vision is changing the world.
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.
- 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
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 neural networks (CNNs), and recurrent neural 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 Deep Learning for Vision Systems book, you’ll gain the essential skills for building amazing end-to-end CV projects that solve real-world problems.