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Hands-On Natural Language Processing with Python

You're reading from   Hands-On Natural Language Processing with Python A practical guide to applying deep learning architectures to your NLP applications

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Product type Paperback
Published in Jul 2018
Publisher Packt
ISBN-13 9781789139495
Length 312 pages
Edition 1st Edition
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Authors (5):
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Rajalingappaa Shanmugamani Rajalingappaa Shanmugamani
Author Profile Icon Rajalingappaa Shanmugamani
Rajalingappaa Shanmugamani
Chaitanya Joshi Chaitanya Joshi
Author Profile Icon Chaitanya Joshi
Chaitanya Joshi
Auguste Byiringiro Auguste Byiringiro
Author Profile Icon Auguste Byiringiro
Auguste Byiringiro
Rajesh Arumugam Rajesh Arumugam
Author Profile Icon Rajesh Arumugam
Rajesh Arumugam
Karthik Muthuswamy Karthik Muthuswamy
Author Profile Icon Karthik Muthuswamy
Karthik Muthuswamy
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Table of Contents (15) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Text Classification and POS Tagging Using NLTK 3. Deep Learning and TensorFlow 4. Semantic Embedding Using Shallow Models 5. Text Classification Using LSTM 6. Searching and DeDuplicating Using CNNs 7. Named Entity Recognition Using Character LSTM 8. Text Generation and Summarization Using GRUs 9. Question-Answering and Chatbots Using Memory Networks 10. Machine Translation Using the Attention-Based Model 11. Speech Recognition Using DeepSpeech 12. Text-to-Speech Using Tacotron 13. Deploying Trained Models 14. Other Books You May Enjoy

TensorFlow Serving

TensorFlow Serving is a project by Google that deploys models to production environments. TensorFlow Serving offers the following advantages:

  • High speed of inference with good latency
  • Concurrency, providing good throughput
  • Model version management, so models can be swapped without a downtime in production

These advantages make TensorFlow Serving a great tool for deployment to the cloud. TensorFlow is served by a gRPC server. gRPC is a remote procedure call system from Google. Since most of the production environments run on Ubuntu, the easiest way to install TensorFlow Serving is by using apt-get, as follows:

sudo apt-get install tensorflow-model-serving

It can also be compiled from a source in other environments. Due to the prevalence of using Docker for deployment, it's easier to build it as an Ubuntu image. For installation-related guidance, please...

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