Computer vision is a technique by which machines gain human-level ability to visualize, process, and analyze images or videos. This book will focus on using TensorFlow to develop and train deep neural networks to solve advanced computer vision problems and deploy solutions on mobile and edge devices.
You will start with the key principles of computer vision and deep learning and learn about various models and architectures, along with their pros and cons. You will cover various architectures, such as VGG, ResNet, Inception, R-CNN, YOLO, and many more. You will use various visual search methods using transfer learning. The book will help you to learn about various advanced concepts of computer vision, including semantic segmentation, image inpainting, object tracking, video segmentation, and action recognition. You will explore how various machine learning and deep learning concepts can be applied in computer vision tasks such as edge detection and face recognition. Later in the book, you will focus on performance tuning to optimize performance, deploying dynamic models to improve processing power, and scaling to handle various computer vision challenges.
By the end of the book, you will have an in-depth understanding of computer vision and will know how to develop models to automate tasks.