English | Jul. 2018 | ISBN: 178913949X | 312 Pages | EPUB
Foster your NLP applications with the help of deep learning, NLTK, and TensorFlow

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.

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

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