Deep Learning for Vision Systems

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 Elgendys expert instruction and illustration of real-world projects, youll finally grok state-of-the-art deep learning techniques, so you can build, contribute to, and lead in the exciting realm of computer vision!

Key Features

  Introduction to computer vision

  Deep learning and neural network

  Transfer learning and advanced CNN architectures

  Image classification and captioning

For readers with intermediate Python, math and machine learning

skills.

About the technology

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.

Mohamed Elgendy is the head of engineering at Synapse Technology, a leading AI company that builds proprietary computer vision applications to detect threats at security checkpoints worldwide. Previously, Mohamed was an engineering manager at Amazon, where he developed and taught the deep learning for computer vision course at Amazons Machine Learning University. He also built and managed Amazons computer vision think tank, among many other noteworthy machine learning accomplishments. Mohamed regularly speaks at many AI conferences like Amazons DevCon, O'Reillys AI conference and Googles I/O.