Starting with the basics, this book teaches you how to choose from the various text pre-processing techniques and select the best model from the several neural network architectures for NLP issues. Deep Learning for Natural Language Processing starts off by highlighting the basic building blocks of the natural language processing domain. The course 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 course, 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.

• Introduction to Natural Language Processing
• Applications of Natural Language Processing
• Introduction to Neural Networks
• Foundations of Convolutional Neural Networks
• Recurrent Neural Networks
• Gated Recurrent Units
• Long Short Term Memory
• State of the art in Natural Language Processing
• A practical NLP project workflow in an organization

• 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

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