Summary
We have reached the end of this chapter, where you have learned how to perform the different steps involved in training a neural network in KNIME Analytics Platform.
We started with common preprocessing steps, including different encodings, normalization, and missing value handling. Next, you learned how to define a neural network architecture by using different Keras layer nodes without writing code. We then moved on to the training of the neural network and you learned how to define the loss function, as well as how you can monitor the learning progress, apply the network to new data, and extract the predictions.
Each section closed with small example sessions, preparing you to perform all these steps on your own.
In the next chapter, you will see how these steps can be applied to the first use case of the book: fraud detection using an autoencoder.
