In the previous chapters, we learned about building a neural network and the various parameters that need to be tweaked to ensure that the model built generalizes well. Additionally, we learned about how neural networks can be leveraged to perform image analysis using MNIST data.
In this chapter, we will learn how neural networks can be used for prediction on top of the following:
- Structured dataset
- Categorical output prediction
- Continuous output prediction
- Text analysis
- Audio analysis
Additionally, we will also be learning about the following:
- Implementing a custom loss function
- Assigning higher weights for certain classes of output over others
- Assigning higher weights for certain rows of a dataset over others
- Leveraging a functional API to integrate multiple sources of data
We will learn about all the preceding by going through the following recipes:
- Predicting...
