Deep Learning for Natural Language Processing: Solve your natural language processing problems with smart deep neural networks

Gain the knowledge of various deep neural network architectures and their application areas 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

Applying deep learning approaches to various NLP tasks can take your computational algorithms to a completely new level in terms of speed and accuracy. Deep Learning for Natural Language Processing starts off by highlighting the basic building blocks of the natural language processing domain. The book goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs. As you advance through this deep learning book, you'll study convolutional, recurrent, and recursive neural networks, in addition to covering long short-term memory networks (LSTM). Understanding these networks will help you to implement their models using Keras. In the later chapters, you will be able to develop a trigger word detection application using NLP techniques such as attention model and beam search.

By the end of this book, you will not only have sound knowledge of natural language processing but also be able to select the best text pre-processing and neural network models to solve a number of NLP issues.

What you will learn

• Understand various pre-processing techniques for 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.

Table of Contents

1. Introduction to Natural Language Processing
2. Application of Natural Language Processing
3. Introduction to Neural Networks
4. Foundations of Convolutional Neural Network
5. Recurrent Neural Networks
6. Gated Recurrent Units
7. Long Short-Term Memory (LSTM)
8. State-of-the-Art Natural Language Processing
9. A Practical NLP Project Workflow in an Organization

Karthiek Reddy Bokka is a Speech and Audio Machine Learning Engineer graduated from University of Southern California and currently working for Biamp Systems in Portland. His interests include Deep Learning, Digital Signal and Audio Processing, Natural Language Processing, Computer Vision. He has experience in designing, building, deploying applications with Artificial Intelligence to solve real-world problems with varied forms of practical data, including Image, Speech, Music, unstructured raw data etc.

Shubhangi Hora is a Python developer, Artificial Intelligence enthusiast, and writer. With a background in Computer Science and Psychology, she is particularly interested in mental health related AI. Shubhangi is based in Pune, India and is passionate about furthering natural language processing through machine learning and deep learning. Aside from this, she enjoys the performing arts and is a trained musician.

Tanuj Jain is a data scientist working at a Germany-based company. He has a master's degree in electrical engineering with a focus on statistical pattern recognition. He has been developing deep learning models and putting them in production for commercial use at his current job. Natural language processing is a special interest area for him and he has applied his know-how to classification and sentiment rating tasks.

Monicah Wambugu is the lead Data Scientist at Loanbee, a financial technology company that offers micro-loans by leveraging on data, machine learning and analytics to perform alternative credit scoring. She is a graduate student at the School of Information at UC Berkeley Masters in Information Management and Systems. Monicah is particularly interested in how data science and machine learning can be used to design products and applications that respond to the behavioral and socio-economic needs of target audiences.