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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

Applications

Today, RNNs (for example, LSTM) have been used in a variety of different applications ranging from time series data modeling, image classification, and video captioning, as well as textual analysis. In this section, we will cover some important applications of RNNs for solving different natural language understanding problems.

Language modeling

Language modeling is one of the fundamental problems in natural language understanding (NLU). The core idea of a language model is to model important distributional properties of the words in a given language. Once such a model is learnt, it can be applied to a sequence of new words to generate the most likely next word token given the learned distributional representation. More formally, a language model computes a joint probability over a sequence of words as follows:

Estimating this probability is computationally expensive, hence a number of estimation techniques exist, which make certain assumptions about the time range dependence of...

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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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