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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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Understanding natural language processing for image captioning

As natural language has to be generated from the image, getting familiar with natural language processing (NLP) becomes important. The concept of NLP is a vast subject, and hence we will limit our scope to topics that are relevant to image captioning. One form of natural language is text. The text is a sequence of words or characters. The atomic element of text is called token, which is a sequence of characters. A character is an atomic element of text. 

In order to process any natural language in the form of text, the text has to be preprocessed by removing punctuation, brackets and so on. Then, the text has to be tokenized into words by separating them into spaces. Then, the words have to be converted to vectors. Next, we will see how this vector conversion can help.  

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