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This section covers the fundamentals of computer vision and deep learning, with the help of concrete TensorFlow examples. Starting with a presentation of these technical domains, the first chapter will then walk you through the inner workings of neural networks. This section continues with an introduction to the instrumental features of TensorFlow 2 and Keras, and their key concepts and ecosystems. It ends with a description of machine learning techniques adopted by computer vision experts.

The following chapters will be covered in this section:

Chapter 1, Computer Vision and Neural Networks
Chapter 2, TensorFlow Basics and Training a Model
Chapter 3, Modern Neural Networks

Key benefits

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

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.

Who is this book for?

If you’re 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 you’re an expert curious about the new TensorFlow 2 features, you’ll 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.

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

What do you get with Print?