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

Bayesian deep learning


We now have a whole set of models that can make forecasts on time series. But are the point estimates that these models give sensible estimates or just random guesses? How certain is the model? Most classic probabilistic modeling techniques, such as Kalman filters, can give confidence intervals for predictions, whereas regular deep learning cannot do this. The field of Bayesian deep learning combines Bayesian approaches with deep learning to enable models to express uncertainty.

The key idea in Bayesian deep learning is that there is inherent uncertainty in the model. Sometimes this is done by learning a mean and standard deviation for weights instead of just a single weight value. However, this approach increases the number of parameters required, so it did not catch on. A simpler hack that allows us to turn regular deep networks into Bayesian deep networks is to activate dropout during prediction time and then make multiple predictions.

In this section, we will be...