Book Image

Hands-On Computer Vision with TensorFlow 2

By : Benjamin Planche, Eliot Andres
Book Image

Hands-On Computer Vision with TensorFlow 2

By: Benjamin Planche, Eliot Andres

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)
Free Chapter
1
Section 1: TensorFlow 2 and Deep Learning Applied to Computer Vision
5
Section 2: State-of-the-Art Solutions for Classic Recognition Problems
9
Section 3: Advanced Concepts and New Frontiers of Computer Vision
14
Assessments

Discovering convolutional neural networks

In the first part of this chapter, we will present CNNs, also known as ConvNets, and explain why they have become omnipresent in vision tasks.

Neural networks for multidimensional data

CNNs were introduced to solve some of the shortcomings of the original neural networks. In this section, we will address these issues and present how CNNs deal with them.

Problems with fully connected networks

Through our introductory experiment in Chapter 1, Computer Vision and Neural Networks, and Chapter 2, TensorFlow Basics and Training a Model...