This chapter will take a deep dive into the development of classic deep learning algorithms to train and deploy models based on unstructured data, exploring libraries and algorithms as well. The examples will be focused on the particularities and advantages of using Databricks for DL, creating DL models. In this chapter, we will learn about how we can efficiently train deep learning models in Azure Databricks and implementations of the different libraries that we have available to use.
The following topics will be introduced in this chapter:
- Loading data for deep learning
- Managing data using TFRecords
- Automating scheme inference
- Using Petastorm for distributed learning
- Reading a dataset
- Data preprocessing and featurization
This chapter will have more of a focus on deep learning models rather than machine learning ones. The main distinction is that we will focus more on handling large amounts of unstructured...