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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

Product type Paperback

Published in Jul 2018

Publisher Packt

ISBN-13 9781789139495

Length 312 pages

Edition 1st Edition

Languages

Processing

Tools

NLTK

Concepts

Deep Learning

Authors (5):

Shanmugamani

Arumugam

Byiringiro

Joshi

Muthuswamy

+1 more

View More author details

Table of Contents (20) Chapters

Title Page

Packt Upsell

Foreword

Contributors

Preface

1. Getting Started

2. Text Classification and POS Tagging Using NLTK FREE CHAPTER

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

1. Other Books You May Enjoy

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Index

Deploying on mobile devices

Deploying models in a mobile environment has varying advantages, such as privacy and zero latency, as compared to the cloud. Famous mobile platforms, such as iPhone and Android, provide a lot of APIs that facilitate the deployment of models to mobile environments.

iPhone

Apple has introduced CoreML2 for ML-related apps. More details can be found at https://developer.apple.com/machine-learning/. There is a specific framework for natural language processing (NLP). There are pre-built APIs for tokenization, identifying a language, parts of speech (POS), named entity recognition, and so on. Custom models can be trained with as well. The use of CoreML models is fast in Apple devices. The data need not leave the device for inference. Most of the TensorFlow models can be converted to CoreML models.

Android

Android apps can use TensorFlow models directly without a lot of modification. Models can be exported (similar to exportation in TensorFlow Serving) and used in the apps...

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Authors (5)

Shanmugamani

Shanmugamani

Rajalingappaa Shanmugamani is currently working as an Engineering Manager for a Deep learning team at Kairos. Previously, he worked as a Senior Machine Learning Developer at SAP, Singapore and worked at various startups in developing machine learning products. He has a Masters from Indian Institute of TechnologyMadras. He has published articles in peer-reviewed journals and conferences and submitted applications for several patents in the area of machine learning. In his spare time, he coaches programming and machine learning to school students and engineers.

See other products by Shanmugamani

Arumugam

Arumugam

Rajesh Arumugam is an ML developer at SAP, Singapore. Previously, he developed ML solutions for smart city development in areas such as passenger flow analysis in public transit systems and optimization of energy consumption in buildings when working with Centre for Social Innovation at Hitachi Asia, Singapore. He has published papers in conferences and has pending patents in storage and ML. He holds a PhD in computer engineering from Nanyang Technological University, Singapore.

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Byiringiro

Byiringiro

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Joshi

Joshi

See other products by Joshi

Muthuswamy

Muthuswamy

See other products by Muthuswamy

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