Book Image

Machine Learning for Finance

By : Jannes Klaas
Book Image

Machine Learning for Finance

By: Jannes Klaas

Overview of this book

Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including insurance, transactions, and lending. This book explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself. The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on advanced machine learning concepts and ideas that can be applied in a wide variety of ways. The book systematically explains how machine learning works on structured data, text, images, and time series. You'll cover generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. Later chapters will discuss how to fight bias in machine learning. The book ends with an exploration of Bayesian inference and probabilistic programming.
Table of Contents (15 chapters)
Machine Learning for Finance
Contributors
Preface
Other Books You May Enjoy
Index

Chapter 8. Privacy, Debugging, and Launching Your Products

Over the course of the last seven chapters we've developed a large toolbox of machine learning algorithms that we could use for machine learning problems in finance. To help round-off this toolbox, we're now going to look at what you can do if your algorithms don't work.

Machine learning models fail in the worst way: silently. In traditional software, a mistake usually leads to the program crashing, and while they're annoying for the user, they are helpful for the programmer. At least it's clear that the code failed, and often the developer will find an accompanying crash report that describes what went wrong. Yet as you go beyond this book and start developing your own models, you'll sometimes encounter machine learning code crashes too, which, for example, could be caused if the data that you fed into the algorithm had the wrong format or shape.

These issues can usually be debugged by carefully tracking which shape the data had...