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.
You Will Learn How To:
- 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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Requirements
Strong working knowledge of Python, linear algebra, and machine learning is a must.
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Who Should Attend This Course
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.