Book Image

Hands-On Natural Language Processing with Python

By : Rajesh Arumugam, Rajalingappaa Shanmugamani, Auguste Byiringiro, Chaitanya Joshi, Karthik Muthuswamy
Book Image

Hands-On Natural Language Processing with Python

By: Rajesh Arumugam, Rajalingappaa Shanmugamani, Auguste Byiringiro, Chaitanya Joshi, Karthik Muthuswamy

Overview of this book

Natural language processing (NLP) has found its application in various domains, such as web search, advertisements, and customer services, and with the help of deep learning, we can enhance its performances in these areas. Hands-On Natural Language Processing with Python teaches you how to leverage deep learning models for performing various NLP tasks, along with best practices in dealing with today’s NLP challenges. To begin with, you will understand the core concepts of NLP and deep learning, such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), semantic embedding, Word2vec, and more. You will learn how to perform each and every task of NLP using neural networks, in which you will train and deploy neural networks in your NLP applications. You will get accustomed to using RNNs and CNNs in various application areas, such as text classification and sequence labeling, which are essential in the application of sentiment analysis, customer service chatbots, and anomaly detection. You will be equipped with practical knowledge in order to implement deep learning in your linguistic applications using Python's popular deep learning library, TensorFlow. By the end of this book, you will be well versed in building deep learning-backed NLP applications, along with overcoming NLP challenges with best practices developed by domain experts.

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Table of Contents (15 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Getting Started
Getting Started
Basic concepts and terminologies in NLP
Applications of NLP
Summary
2
Text Classification and POS Tagging Using NLTK
Text Classification and POS Tagging Using NLTK
Installing NLTK and its modules
Text preprocessing and exploratory analysis
Exploratory analysis of text
POS tagging
Training a sentiment classifier for movie reviews
Training a bag-of-words classifier
Summary
3
Deep Learning and TensorFlow
Deep Learning and TensorFlow
Deep learning
TensorFlow
Summary
4
Semantic Embedding Using Shallow Models
Semantic Embedding Using Shallow Models
Word vectors
From word to document embeddings
Sentence2vec
Doc2vec
Summary
5
Text Classification Using LSTM
Text Classification Using LSTM
Data for text classification
Topic modeling
Deep learning meta architecture for text classification
Identifying spam in YouTube video comments using RNNs
Classifying news articles by topic using a CNN
Transfer learning using GloVe embeddings
Multi-label classification
Summary
6
Searching and DeDuplicating Using CNNs
Searching and DeDuplicating Using CNNs
Data
Training the model
Summary
7
Named Entity Recognition Using Character LSTM
Named Entity Recognition Using Character LSTM
NER with deep learning
Summary
8
Text Generation and Summarization Using GRUs
Text Generation and Summarization Using GRUs
Generating text using RNNs
Text summarization
Summary
9
Question-Answering and Chatbots Using Memory Networks
Question-Answering and Chatbots Using Memory Networks
The Question-Answering task
Memory networks for Question-Answering
Extending memory networks for dialog modeling
Summary
10
Machine Translation Using the Attention-Based Model
Machine Translation Using the Attention-Based Model
Overview of machine translation
Summary
11
Speech Recognition Using DeepSpeech
Speech Recognition Using DeepSpeech
Overview of speech recognition
Building an RNN model for speech recognition
Summary
12
Text-to-Speech Using Tacotron
Text-to-Speech Using Tacotron
Overview of text to speech
TTS in deep learning
Implementation of Tacotron with Keras
Summary
13
Deploying Trained Models
Deploying Trained Models
Increasing performance
TensorFlow Serving
Deploying in the cloud
Deploying on mobile devices
Summary
14
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Deploying Trained Models

In this chapter, you will learn how to deploy trained deep learning models to production environments on various platforms, such as cloud and mobile. For cloud deployment, the latency and throughput are important. The latency has to be at a minimum, and the throughput has to be high. The performance largely depends on the model and hardware. There are several optimizations available for CPU and GPU. For mobile platforms, speed, and energy consumption are important.

In this chapter, you will learn techniques to meet your deployment goals through the following topics:

  • Increasing performance by changing models
  • Using the TensorFlow serving tool
  • Deploying to cloud services, such as AWS, GCP, and Azure
  • Deploying to mobile devices, such as iPhone, Android, and Tegra
  • The impact of hardware on performance
Previous Section
End of Section 1
Next Section