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Deep Learning for Computer Vision

Deep Learning for Computer Vision

By : Shanmugamani
3.2 (22)
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Deep Learning for Computer Vision

Deep Learning for Computer Vision

3.2 (22)
By: Shanmugamani

Overview of this book

Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning. In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation.
Table of Contents (12 chapters)
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Getting Started

Computer vision is the science of understanding or manipulating images and videos. Computer vision has a lot of applications, including autonomous driving, industrial inspection, and augmented reality. The use of deep learning for computer vision can be categorized into multiple categories: classification, detection, segmentation, and generation, both in images and videos. In this book, you will learn how to train deep learning models for computer vision applications and deploy them on multiple platforms. We will use TensorFlow, a popular python library for deep learning throughout this book for the examples. In this chapter, we will cover the following topics:

  • The basics and vocabulary of deep learning
  • How deep learning meets computer vision?
  • Setting up the development environment that will be used for the examples covered in this book
  • Getting a feel for TensorFlow, along with its powerful tools, such as TensorBoard and TensorFlow Serving
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