HANDS-ON COMPUTER VISION WITH TENSORFLOW 2 IBD

Sinopsi

A practical guide to building high performance systems for object detection, segmentation, video processing, smartphone applications, and moreáKey Features:Discover how to build, train, and serve your own deep neural networks with TensorFlow 2 and KerasApply modern solutions to a wide range of applications such as object detection and video analysisLearn how to run your models on mobile devices and web pages and improve their performanceBook Description:Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. This book will help you explore TensorFlow 2, the brand new version of GoogleâÇÖs open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks.Hands-On Computer Vision with TensorFlow 2 starts with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. YouâÇÖll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the book demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative adversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts.By the end of the book, you will have both the theoretical understanding and practical skills to solve advanced computer vision problems with TensorFlow 2.0.What you will learn:Create your own neural networks from scratchClassify images with modern architectures including Inception and ResNetDetect and segment objects in images with YOLO, Mask R-CNN, and U-NetTackle problems faced when developing self-driving cars and facial emotion recognition systemsBoost your applicationâÇÖs performance with transfer learning, GANs, and domain adaptationUse recurrent neural networks (RNNs) for video analysisOptimize and deploy your networks on mobile devices and in the browserWho this book is for:If youâÇÖre new to deep learning and have some background in Python programming and image processing, like reading/writing image files and editing pixels, this book is for you. Even if youâÇÖre an expert curious about the new TensorFlow 2 features, youâÇÖll find this book useful.While some theoretical concepts require knowledge of algebra and calculus, the book covers concrete examples focused on practical applications such as visual recognition for self-driving cars and smartphone apps.