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You're reading from  Natural Language Processing with Python Quick Start Guide

Product typeBook
Published inNov 2018
Reading LevelIntermediate
PublisherPackt
ISBN-139781789130386
Edition1st Edition
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Nirant Kasliwal
Nirant Kasliwal
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Nirant Kasliwal

Nirant Kasliwal maintains an awesome list of NLP natural language processing resources. GitHub's machine learning collection features this as the go-to guide. Nobel Laureate Dr. Paul Romer found his programming notes on Jupyter Notebooks helpful. Nirant won the first ever NLP Google Kaggle Kernel Award. At Soroco, image segmentation and intent categorization are the challenges he works with. His state-of-the-art language modeling results are available as Hindi2vec.
Read more about Nirant Kasliwal

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Web Deployments

So far, we have been focused on getting something to work for the very first time and then making incremental updates. These updates are almost always geared toward better techniques and better usability. But, how do we expose them to the user? One way to do this is via REST endpoints.

In this chapter, we are going to cover the following topics:

  • Training a model, and writing some neater utils for data I/O
  • Building a predict function, separated from training
  • Exposing what we have covered using a Flask REST endpoint
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Natural Language Processing with Python Quick Start Guide
Published in: Nov 2018Publisher: PacktISBN-13: 9781789130386

Author (1)

author image
Nirant Kasliwal

Nirant Kasliwal maintains an awesome list of NLP natural language processing resources. GitHub's machine learning collection features this as the go-to guide. Nobel Laureate Dr. Paul Romer found his programming notes on Jupyter Notebooks helpful. Nirant won the first ever NLP Google Kaggle Kernel Award. At Soroco, image segmentation and intent categorization are the challenges he works with. His state-of-the-art language modeling results are available as Hindi2vec.
Read more about Nirant Kasliwal