Machine Learning for Finance

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 ...