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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

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Product type Paperback
Published in Jun 2019
Publisher
ISBN-13 9781838550295
Length 372 pages
Edition 1st Edition
Languages
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Authors (4):
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Karthiek Reddy Bokka Karthiek Reddy Bokka
Author Profile Icon Karthiek Reddy Bokka
Karthiek Reddy Bokka
Monicah Wambugu Monicah Wambugu
Author Profile Icon Monicah Wambugu
Monicah Wambugu
Tanuj Jain Tanuj Jain
Author Profile Icon Tanuj Jain
Tanuj Jain
Shubhangi Hora Shubhangi Hora
Author Profile Icon Shubhangi Hora
Shubhangi Hora
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Toc

Table of Contents (11) Chapters Close

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

Data Acquisition

A big contribution toward determining the performance of any machine learning model is the quality and quantity of the data.

Usually, a data warehousing team/infrastructure team (DWH) is responsible for maintaining the data-related infrastructure at a company. The team takes care that data is never lost, that the underlying infrastructure is stable, and that data is always available for any team that might be interested in using it. The data science team, being one of the consumers of the data, contacts the DWH team, which grants them access to a database that contains all the reviews for various items in the product catalog of the company.

Typically, there are multiple data fields/tables in the database, some of which may not be important for the machine learning model development.

A data engineer (a part of the DWH team/member of another team/member of your team) then connects to the database, processes the data into a tabular format, and generates a flat file in the csv...

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