2 Introduction to the example problem and Pandas dataframes

In this chapter, you’ll learn about the options that you can pick for a deep learning environment and how to bring tabular structured data into your Python program. You will get an overview of Pandas, the Pythonic facility for manipulating tabular structured data. You will also learn about the major example that is used throughout this book to demonstrate deep learning for structured data, including details about the dataset used in the major example and the overall structure of the code for this example. Then you will get more details on the objections to deep learning with structured data that were introduced in chapter 1. Finally, we will take a peek ahead and go through a round of training the deep learning model to whet your appetite for the rest of the extended example that is examined in chapters 3-8.

2.1 Development environment options for deep learning

2.2 Code for exploring Pandas

2.3 Pandas dataframes in Python

2.4 Ingesting CSV files into Pandas dataframes

2.5 Using Pandas to do what you would do with SQL

2.6 The major example: Predicting streetcar delays

2.7 Why is a real-world dataset critical for learning about deep learning?

2.8 Format and scope of the input dataset

2.9 The destination: An end-to-end solution

2.10 More details on the code that makes up the solutions

2.11 Development environments: Vanilla vs. deep-learning-enabled

2.12 A deeper look at the objections to deep learning

2.13 How deep learning has become more accessible

2.14 A first taste of training a deep learning model

Summary