Hands-on Natural Language Processing with Python

Foster your NLP applications with the help of deep learning, NLTK, and TensorFlow

Key Features

• Weave neural networks into linguistic applications across various platforms
• Perform NLP tasks and train its models using NLTK and TensorFlow
• Boost your NLP models with strong deep learning architectures such as CNNs and RNNs

Book Description

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.

What you will learn

• Implement semantic embedding of words to classify and find entities
• Convert words to vectors by training in order to perform arithmetic operations
• Train a deep learning model to detect classification of tweets and news
• Implement a question-answer model with search and RNN models
• Train models for various text classification datasets using CNN
• Implement WaveNet a deep generative model for producing a natural-sounding voice
• Convert voice-to-text and text-to-voice
• Train a model to convert speech-to-text using DeepSpeech

Who this book is for

Hands-on Natural Language Processing with Python is for you if you are a developer, machine learning or an NLP engineer who wants to build a deep learning application that leverages NLP techniques. This comprehensive guide is also useful for deep learning users who want to extend their deep learning skills in building NLP applications. All you need is the basics of machine learning and Python to enjoy the book.

Table of Contents

1. Getting Started
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

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.

Rajalingappaa Shanmugamani is a deep learning lead at SAP, Singapore. Previously, he worked and consulted at various start-ups for developing computer vision products. He has a masters from IIT Madras, where his thesis was based on applications of computer vision in manufacturing. He has published articles in peer-reviewed journals and conferences and applied for a few patents in ML. In his spare time, he teaches programming and machine learning to school students and engineers.