In this chapter, the following recipes will be covered:
- Creating a dataframe in PySpark
- Manipulating columns in a PySpark dataframe
- Converting a PySpark dataframe into an array
- Visualizing the array in a scatterplot
- Setting up weights and biases for input into the neural network
- Normalizing the input data for the neural network
- Validating array for optimal neural network performance
- Setting up the activation function with sigmoid
- Creating the sigmoid derivative function
- Calculating the cost function in a neural network
- Predicting gender based on height and weight
- Visualizing prediction scores
