Hands-On Computer Vision with Tensorflow 2

A practical guide to building high performance systems for object detection, segmentation, video processing, smartphone applications, and more

Key Features

Discover how to build, train, and serve your own deep neural networks with TensorFlow 2 and Keras

Apply modern solutions to a wide range of applications such as object detection and video analysis

Learn how to run your models on mobile devices and web pages and improve their performance



Book 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 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.

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 faced when 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 (RNNs) for video analysis

Optimize and deploy your networks on mobile devices and in the browser