Hands-On Computer Vision with TensorFlow 2: Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras

Eliot Andres

ISBN 10: 1788830644 / ISBN 13: 9781788830645

Edité par Packt Publishing, 2019

A propos de cet article

The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

N° de réf. du vendeur GOR011712782

Détails bibliographiques

• Titre: Hands-On Computer Vision with TensorFlow 2: Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras
• Éditeur: Packt Publishing
• Date de parution: 2019
• Langue: anglais
• ISBN à 10 chiffres: 1788830644
• ISBN à 13 chiffres: 9781788830645
• Reliure: Paperback
• État de l'article: Very Good
• Poids: 739 grammes

A propos de ce titre

Synopsis

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

Who this book is 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.

Table of 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
10. Appendix

À propos de l?auteur

Dr. Benjamin Planche is a passionate research scientist in computer vision and machine learning. His main research efforts focus on data scarcity problems and industrial vision systems, leading to numerous patents and publications at international conferences. He worked in various research labs around the world (including in France, Japan, Germany, and the USA). Benjamin obtained his Ph.D. summa cum laude from the Faculty of Computer Science and Mathematics at the University of Passau, under the supervision of Prof. Dr. Harald Kosch. He also has a double master's degree from INSA-Lyon (France) and the University of Passau (Germany), with first-class honors and a multinational excellence award. He also likes sharing his knowledge and experience on various platforms or applying them to the creation of aesthetic demos. Eliot Andres is a freelance deep learning and computer vision engineer. He has more than 3 years' experience in the field, applying his skills to a variety of industries, such as banking, health, social media, and video streaming. Eliot has a double master's degree from cole des Ponts and Tlcom, Paris. His focus is industrialization: delivering value by applying new technologies to business problems. Eliot keeps his knowledge up to date by publishing articles on his blog and by building prototypes using the latest technologies.