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Deep Learning for Natural Language Processing

You're reading from Deep Learning for Natural Language Processing Solve your natural language processing problems with smart deep neural networks

Product type Paperback

Published in Jun 2019

Publisher

ISBN-13 9781838550295

Length 372 pages

Edition 1st Edition

Languages

Processing

Tools

Processing

Concepts

Deep Learning

Authors (4):

Karthiek Reddy Bokka

Monicah Wambugu

Tanuj Jain

Shubhangi Hora

View More author details

Table of Contents (11) Chapters

About the Book

1. Introduction to Natural Language Processing FREE CHAPTER

2. Applications of Natural Language Processing

3. Introduction to Neural Networks

4. Foundations of Convolutional Neural Network

5. Recurrent Neural Networks

6. Gated Recurrent Units (GRUs)

7. Long Short-Term Memory (LSTM)

8. State-of-the-Art Natural Language Processing

9. A Practical NLP Project Workflow in an Organization

1. Appendix

Recurrent Neural Networks

Learning Objectives

By the end of this chapter, you will be able to:

Describe classical feedforward networks
Differentiate between feedforward neural networks and recurrent neural networks
Evaluate the application of backpropagation through time for recurrent neural networks
Describe the drawbacks of recurrent neural networks
Use recurrent neural networks with keras to solve the author attribution problem

This chapter aims to introduce you to recurrent neural networks and their applications, as well as their drawbacks.

The rest of the chapter is locked

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Authors (4)

Karthiek Reddy Bokka

Karthiek Reddy Bokka

Karthiek Reddy Bokka is a Speech and Audio Machine Learning Engineer graduated from University of Southern California and currently working for Biamp Systems in Portland. His interests include Deep Learning, Digital Signal and Audio Processing, Natural Language Processing, Computer Vision. He has experience in designing, building, deploying applications with Artificial Intelligence to solve real-world problems with varied forms of practical data, including Image, Speech, Music, unstructured raw data etc.

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Monicah Wambugu

Monicah Wambugu

Monicah Wambugu is the lead Data Scientist at Loanbee, a financial technology company that offers micro-loans by leveraging on data, machine learning and analytics to perform alternative credit scoring. She is a graduate student at the School of Information at UC Berkeley Masters in Information Management and Systems. Monicah is particularly interested in how data science and machine learning can be used to design products and applications that respond to the behavioral and socio-economic needs of target audiences.

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Tanuj Jain

Tanuj Jain

Tanuj Jain is a data scientist working at a Germany-based company. He has a masters degree in electrical engineering with a focus on statistical pattern recognition. He has been developing deep learning models and putting them in production for commercial use at his current job. Natural language processing is a special interest area for him and he has applied his know-how to classification and sentiment rating tasks.

See other products by Tanuj Jain

Shubhangi Hora

Shubhangi Hora

Shubhangi Hora is a data scientist, Python developer, and published writer. With a background in computer science and psychology, she is particularly passionate about healthcare-related AI, including mental health. Shubhangi is also a trained musician.

See other products by Shubhangi Hora

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