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Deep Learning Essentials

You're reading from Deep Learning Essentials Your hands-on guide to the fundamentals of deep learning and neural network modeling

Product type Paperback

Published in Jan 2018

Publisher Packt

ISBN-13 9781785880360

Length 284 pages

Edition 1st Edition

Languages

Processing

Tools

Caffe

Concepts

Deep Learning

Authors (3):

Di

Jianing Wei

Anurag Bhardwaj

View More author details

Table of Contents (17) Chapters

Title Page

Copyright and Credits

Packt Upsell

Contributors

Preface

1. Why Deep Learning? FREE CHAPTER

2. Getting Yourself Ready for Deep Learning

3. Getting Started with Neural Networks

4. Deep Learning in Computer Vision

5. NLP - Vector Representation

6. Advanced Natural Language Processing

7. Multimodality

8. Deep Reinforcement Learning

9. Deep Learning Hacks

10. Deep Learning Trends

1. Other Books You May Enjoy

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Index

Visual question answering

The task of visual question answering (VQA) is the task of answering an open-ended text question about a given image. VQA was proposed by Antol and its co-authors in 2015 (https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Antol_VQA_Visual_Question_ICCV_2015_paper.pdf). This task lies at the intersection of computer vision and natural language processing. It requires the understanding of the image and the parsing and understanding of the text question. Due to its multimodality nature and its well-defined quantitative evaluation metric, VQA is considered an important artificial intelligence task. It also has potential practical applications, including helping the visually impaired.

A few examples of the VQA task are illustrated in the following table:

Q: How many giraffes can be seen?

A: 2

Q: Is the bus door open?

A: Yes

Q: If you were to encounter this sign, what would you do?

A: Stop

Several datasets have been proposed for visual question answering, including...

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Authors (3)

Di

Di

Wei Di is a data scientist with years of experience in machine learning and artificial intelligence. She is passionate about creating smart and scalable intelligent solutions that can impact millions of individuals and empower successful business. Currently, she works a staff data scientist in LinkedIn. She was previously associated with eBay Human Language Technology team and eBay Research Labs. Prior to that, she was with ancestry, working on large-scale data mining in the areas of record linkage. She received her PhD from Purdue University in 2011.

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Jianing Wei

Jianing Wei

Jianing Wei is a senior software engineer at Google Research. He works in the area of computer vision and computational imaging. Prior to joining Google in 2013, Jianing worked at Sony US Research Center for four years in the area of 3D computer vision and image processing. Jianing obtained his Ph.D. in Electrical and Computer Engineering from Purdue University in 2010.

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Anurag Bhardwaj

Anurag Bhardwaj

Anurag Bhardwaj currently leads data science efforts at Wiser Solutions, where he focuses on structuring large scale eCommerce inventory. He is particularly interested in using machine learning to solve problems on product category classification, product matching and various related problems in eCommerce. Previously, he worked on image understanding at eBay Research Labs. Anurag received his PhD and MS from the State University of New York at Buffalo and holds a BTech in computer engineering from the National Institute of Technology, Kurukshetra, India.

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