Summary
In this section, we have covered many examples related to how we can extract and improve features that we have available in the data, using methods such as tokenization, polynomial expansion, and one-hot encoding, among others. These methods allow us to prepare our variables for the training of our models and are considered as a part of feature engineering.
Next, we dived into how we can extract features from text using TF-IDF and Word2Vec and how we can handle missing data in Azure Databricks using the PySpark API. Finally, we have finished with an example of how we can train a deep learning model and have it ready for serving and get predictions when posting REST API requests.
In the next chapter, we will focus more on handling large amounts of data for deep learning using TFRecords and Petastorm, as well as on how we can leverage existing models to extract features from new data in Azure Databricks.
