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
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In the last section, we learned about basic RNNs. In theory, simple RNNs should be able to retain even long-term memories. However, in practice, this approach often falls short because of the vanishing gradients problem.

Over the course of many timesteps, the network has a hard time keeping up meaningful gradients. While this is not the focus of this chapter, a more detailed exploration of why this happens can be read in the 1994 paper, Learning long-term dependencies with gradient descent is difficult, available at - - by Yoshua Bengio, Patrice Simard, and Paolo Frasconi.

In direct response to the vanishing gradients problem of simple RNNs, the Long Short-Term Memory (LSTM) layer was invented. This layer performs much better at longer time series. Yet, if relevant observations are a few hundred steps behind in the series, then even LSTM will struggle. This is why we manually included some lagged observations.

Before we dive into details, let...