Summary

In this chapter, you have learned a number of practical tips for debugging and improving your model. Let's recap all of the things that we have looked at:

• Finding flaws in your data that lead to flaws in your learned model

• Using creative tricks to make your model learn more from less data

• Unit testing data in production or training to make sure standards are met

• Being mindful of privacy

• Preparing data for training and avoiding common pitfalls

• Inspecting the model and peering into the "black box"

• Finding optimal hyperparameters

• Scheduling learning rates in order to reduce overfitting

• Monitoring training progress with TensorBoard

• Deploying machine learning products and iterating on them

• Speeding up training and inference

You now have a substantial number of tools in your toolbox that will help you run actual, practical machine learning projects and deploy them in real-life (for example, trading) applications.

Making sure your model works before deploying it is crucial and failure to properly scrutinize...