The machine learning software stack

In this chapter, we will be using a range of different libraries that are commonly used in machine learning. Let's take a minute to look at our stack, which consists of the following software:

• Keras: A neural network library that can act as a simplified interface to TensorFlow.
• NumPy: Adds support for large, multidimensional arrays as well as an extensive collection of mathematical functions.
• Pandas: A library for data manipulation and analysis. It's similar to Microsoft's Excel but in Python, as it offers data structures to handle tables and the tools to manipulate them.
• Scikit-learn: A machine learning library offering a wide range of algorithms and utilities.
• TensorFlow: A dataflow programming library that facilitates working with neural networks.
• Matplotlib: A plotting library.
• Jupyter: A development environment. All of the code examples in this book are available in Jupyter Notebooks.

The majority of this book is dedicated to working with the Keras library, while this chapter makes extensive use of the other libraries mentioned. The goal here is less about teaching you all the tips and tricks of all the different libraries, but more about showing you how they are integrated into the process of creating a predictive model.

Note

Note: All of the libraries needed for this chapter are installed on Kaggle kernels by default. If you are running this code locally, please refer to the setup instructions in Chapter 1, Neural Networks and Gradient-Based Optimization, and install all of the libraries needed.