Summary
In this chapter, you have learned the most important NLP techniques. There was a lot that we've learned, and here's a big list of things we covered in this chapter and everything you should now feel confident about understanding:
Finding named entities
Fine-tuning spaCy's models for your own custom applications
Finding parts of speech and mapping the grammatical structure of sentences
Using regular expressions
Preparing text data for classification tasks
Using techniques such as bag-of-words and TF-IDF for classification
Modeling the topics present in a text with LDA
Using pretrained word embeddings
Building advanced models with the Keras functional API
Training your model to focus on attention
Translating sentences with the seq2seq model
You now have a big set of tools in your toolbox that will allow you to tackle NLP problems. Throughout the rest of this book, you will see some of these techniques again, being used in different contexts to solve hard problems. These techniques are useful across...
