Most convolutional encoders-decoders follow the same template as their fully connected counterparts, but leverage the spatial properties of their locally connected layers for higher-quality results. A typical convolutional AE is presented in one of the Jupyter notebooks. In this subsection, we will cover two more advanced architectures derived from this basic template. Both released in 2015, the FCN and U-Net models are still popular, and are commonly used as components for more complex systems (in semantic segmentation, domain adaptation, and others).
Hands-On Computer Vision with TensorFlow 2
By :
Hands-On Computer Vision with TensorFlow 2
By:
Overview of this book
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 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. You'll 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.
Table of Contents (16 chapters)
Preface
Free Chapter
Section 1: TensorFlow 2 and Deep Learning Applied to Computer Vision
Computer Vision and Neural Networks
TensorFlow Basics and Training a Model
Modern Neural Networks
Section 2: State-of-the-Art Solutions for Classic Recognition Problems
Influential Classification Tools
Object Detection Models
Enhancing and Segmenting Images
Section 3: Advanced Concepts and New Frontiers of Computer Vision
Training on Complex and Scarce Datasets
Video and Recurrent Neural Networks
Optimizing Models and Deploying on Mobile Devices
Migrating from TensorFlow 1 to TensorFlow 2
Assessments
Other Books You May Enjoy
How would like to rate this book
Customer Reviews
