Summary

In this chapter, we broadly explored NLP to get an impression of the kind of tasks involved in building a good NLP-based system. First, we explained why we need NLP and then discussed various tasks of NLP to generally understand the objective of each task and how difficult it is to succeed at these tasks.

Next, we looked at the classical approach of solving NLP and went into the details of the workflow using an example of generating sport summaries for football games. We saw that the traditional approach usually involves cumbersome and tedious feature engineering. For example, in order to check the correctness of a generated phrase, we might need to generate a parse tree for that phrase. Next, we discussed the paradigm shift that transpired with deep learning and saw how deep learning made the feature engineering step obsolete. We started with a bit of time-travelling to go back to the inception of deep learning and artificial neural networks and worked our way to the massive modern networks with hundreds of hidden layers. Afterward, we walked through a simple example illustrating a deep model—a multilayer perceptron model—to understand the mathematical wizardry taking place in such a model (on the surface of course!).

With a nice foundation to both traditional and modern ways of approaching NLP, we then discussed the roadmap to understand the topics we will be covering in the book, from learning word embeddings to mighty LSTMs, generating captions for images to neural machine translators! Finally, we set up our environment by installing Python, scikit-learn, Jupyter Notebook, and TensorFlow.

In the next chapter, you will learn the basics of TensorFlow. By the end of the chapter, you should be comfortable with writing a simple algorithm that can take some input, transform the input through a defined function and output the result.