Get well versed with state-of-the-art techniques to tailor training processes and boost the performance of computer vision models using machine learning and deep learning techniques Key Features * Develop, train, and use deep learning algorithms for computer vision tasks using TensorFlow 2.x * Discover practical recipes to overcome various challenges faced while building computer vision models * Enable machines to gain a human-level understanding to recognize and analyze digital images and videos Book Description Computer vision is a scientific field that enables machines to identify and process digital images and videos. This book focusses on independent recipes to help you perform various computer vision tasks using TensorFlow. The book begins by taking you through the basics of deep learning for computer vision, along with covering TensorFlow 2.x's Key Features, such as Keras and tf.data.Dataset APIs. You'll then learn about the ins and outs of common computer vision tasks, such as image classification, transfer learning, image enhancing and styling, and object detection. The book also covers autoencoders in domains such as image restoration, inverse image search indexes, and image denoising, while offering insights into various architectures used in the recipes, such as convolutional neural networks (CNNs), region-based CNNs (R-CNNs), DenseNet, VGGNet, and you only look once (YOLO). Moving on, you'll discover tips and tricks to solve any problems faced while building various computer vision applications. Finally, you'll delve into more advanced topics such as GANs, video processing, and AutoML, concluding with a section focused on techniques to help you boost the performance of your networks. By the end of this TensorFlow book, you'll be able to confidently tackle a wide range of computer vision problems using TensorFlow 2.x, as well as apply more advanced techniques such as AutoML to challenging projects. What you will learn * Understand how to detect objects using state-of-the-art models such as YOLOv3 * Use AutoML to predict gender and age from images * Segment images using different approaches such as FCNs and generative models * Learn how to improve your network's performance using rank-N accuracy, label smoothing, and test time augmentation * Enable machines to recognize people's emotions in real time using webcams * Access and re-use advanced TFHub models to perform image classification and object detection * Generate captions to images using CNNs and RNNs Who This Book Is For This book is for computer vision developers, computer vision engineers, and deep learning practitioners looking for go-to solutions for solving various problems faced in computer vision. You will discover how to employ modern machine learning (ML) techniques and deep learning architectures to perform a plethora of computer vision tasks. Basic knowledge of Python programming and computer vision is required.

Book Information
ISBN 9781838829131
Author Jesus Martinez
Format Paperback
Page Count 415
Imprint Packt Publishing Limited
Publisher Packt Publishing Limited
Weight(grams) 191g