In this chapter, we'll use the prepared data from the previous chapter to train some of the RNN and VAE networks. Machine learning training is a finicky process involving a lot of tuning, experimentation, and back and forth between your data and your model. We'll learn to tune hyperparameters, such as batch size, learning rate, and network size, to optimize network performance and training time. We'll also show common training problems such as overfitting and models not converging. Once a model's training is complete, we'll show how to use the trained model to generate new sequences. Finally, we'll show how to use Google Cloud Platform to train models faster on the cloud.
The following topics will be covered in this chapter:
- Choosing the model and configuration
- Training and tuning a model
- Using Google Cloud Platform
