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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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Deployment in the cloud


The models have to be deployed in the cloud for several applications. We will look at major cloud service providers for this purpose.

AWS

The Amazon Web Services (AWS) extends support to the development and deployment of TensorFlow-based models. Sign up for AWS at https://aws.amazon.com/ and select one of the Amazon Machine Images (AMI). AMIs are images of machines with all the required software installed. You need not worry about installing the packages. AWS provides Deep Learning AMI (DLAMI) for ease of training and deploying deep learning models. There are several options to choose from. Here, we will use Conda as it comes with several packages required for running TensorFlow. There are two options for Python: version 2 and 3. The following code will activate TensorFlow with Keras 2 on Python 3 on CUDA 8:

source activate tensorflow_p36

The following code will activate TensorFlow with Keras 2 on Python 2 on CUDA 8:

source activate tensorflow_p27

Note

You can visit https...

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