Deep Learning for Natural Language Processing

Gain knowledge of various deep neural network architectures and their areas of application to conquer your NLP issues

Key Features

• Gain insights into the basic building blocks of natural language processing
• Learn how to select the best deep neural network to solve your NLP problems
• Explore convolutional and recurrent neural networks and long short-term memory networks

Book Description

What you will learn

• Understand various preprocessing techniques for solving deep learning problems
• Build a vector representation of text using word2vec and GloVe
• Create a named entity recognizer and parts-of-speech tagger with Apache OpenNLP
• Build a machine translation model in Keras
• Develop a text generation application using LSTM
• Build a trigger word detection application using an attention model

Who this book is for

If you’re an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is just the book for you. Strong working knowledge of Python, linear algebra, and machine learning is a must.