Mastering Computer Vision with TensorFlow 2.x

focuses on how machine learning and deep learning concepts can be used to perform tasks such as edge detection and face recognition. You'll then discover how to develop powerful neural network models on your local PC and on various cloud platforms. Finally, you'll be able to perform model optimization methods to deploy models in edge devices for real-time inference. By the end of this book, you'll have a solid understanding of computer vision and be able to confidently develop models to automate tasks. What you will learn * Explore methods for feature extraction and image retrieval and visualize different layers of the neural network model * Use TensorFlow for various visual search methods for real-world scenarios * Build neural networks or adjust parameters to optimize the performance of models * Understand TensorFlow DeepLab to perform semantic segmentation on images and DCGAN for image inpainting * Evaluate your model, and optimize and integrate it into your application to operate at scale * Get up to speed with techniques for performing manual and automated image annotation Who This Book Is For This book is for computer vision professionals, image processing professionals, machine learning engineers and AI developers who have some knowledge of machine learning and deep learning and want to build expert-level computer vision applications. In addition to familiarity with TensorFlow, Python knowledge will be required to get started with this book.