Hands-On Computer Vision with TensorFlow 2: Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras
Authors: Benjamin Planche – Eliot Andres
ISBN-10: 1788830644
ISBN-13: 9781788830645
Publication Date 出版日期: 2019-05-30
Print Length 页数: 372 pages
Publisher finelybook 出版社: Packt
Book Description
By finelybook
A practical guide to building high performance systems for object detection,segmentation,video processing,smartphone applications,and more.
Computer vision solutions are becoming increasingly common,making their way in fields such as health,automobile,social media,and robotics. This book will help you explore TensorFlow 2,the brand new version of Google’s 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,and 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 Auto-Encoders (VAEs) to create and edit images,and 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 scratch
Classify images with modern architectures including Inception and ResNet
Detect and segment objects in images with YOLO,Mask R-CNN,and U-Net
Tackle problems in developing self-driving cars and facial emotion recognition systems
Boost your application’s performance with transfer learning,GANs,and domain adaptation
Use recurrent neural networks for video analysis
Optimize and deploy your networks on mobile devices and in the browser
contents
1 Computer Vision and Neural Networks
2 TensorFlow Basics and Training a Model
3 Modern Neural Networks
4 Influential Classification Tools
5 Object Detection Models
6 Enhancing and Segmenting Images
7 Training on Complex and Scarce Datasets
8 Video and Recurrent Neural Networks
9 Optimizing Models and Deploying on Mobile Devices
