Deep Learning for Natural Language Processing

▶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

▶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

▶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.

▶Description
This book will start with the basic building blocks of natural language processing domain. It will introduce the problems that can be solved using the state-of-the-art Neural Network models. It will cover deeply the necessary pre-processing needed in the text processing tasks. The book will cover some hot topics in the NLP domain, which include Convolutional Neural Networks, Recurrent Neural Networks, and Long Short Term Memory Networks. The audience of this book will understand the importance of text pre-processing, and hyper parameter tuning as well.

▶Audience
Aspiring data scientists and engineers who want to be introduced to deep learning in the domain of natural language processing.

They will start with the basics of natural language processing concepts and will gradually dive deeper into the concepts of Neural Networks and their application in text processing problems. They will get to learn different Neural Network architectures along with their application areas. Strong knowledge in Python and linear algebra skills are expected.

▶Approach
Deep learning for natural language processing will start with the very basic concepts of natural language processing. Once the basic concepts are introduced, the audience will gradually be made aware of the applications and problems in the real world where NLP techniques are applicable. Once the user understands the problem domain, the approach for developing the solution will be introduced. As part of solution-based approach, basic building blocks of Neural Networks are discussed. Eventually, modern architectures of various Neural Networks are elaborated with their corresponding application areas with examples.