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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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Generative Models

Generative models have become an important application in computer vision. Unlike the applications discussed in previous chapters that made predictions from images, generative models can create an image for specific objectives. In this chapter, we will understand:

  • The applications of generative models
  • Algorithms for style transfer
  • Training a model for super-resolution of images
  • Implementation and training of generative models
  • Drawbacks of current models 

By the end of the chapter, you will be able to implement some great applications for transferring style and understand the possibilities, as well as difficulties, associated with generative models.

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