Hands-On Computer Vision with TensorFlow 2: Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras
Language: English
Published by Packt Publishing, 2019
- Softcover
- Used
Condition: Used - Very good
US$ 22.07
Quantity: 2 available
Add to basketItem description from seller
Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.
Seller Inventory # M01788830644-V
- Title
- Hands-On Computer Vision with TensorFlow 2: Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras
- Author
- Benjamin Planche
- Publisher
- Packt Publishing
- Publication year
- 2019
- Condition
- very good
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1788830644
- ISBN 13
- 9781788830645
- Series
- Book 101 of 300: Lecture Notes in Computer Science
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
- Computer Vision and Neural Networks
- TensorFlow Basics and Training a Model
- Modern Neural networks
- Influential Classification Tools
- Object Detection Models
- Enhancing and Segmenting Images
- Training on Complex and Scarce Datasets
- Video and Recurrent Neural Networks
- Optimizing Models and Deploying on Mobile Devices
- Appendix
"Synopsis" may belong to another edition of this title.
About the Author
"About the title" may belong to another edition of this title.
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