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

Performance tips


In many financial applications, speed is of the essence. Machine learning, especially deep learning, has a reputation for being slow. However, recently, there have been many advances in hardware and software that enable faster machine learning applications.

Using the right hardware for your problem

A lot of progress in deep learning has been driven by the use of graphics processing units (GPUs). GPUs enable highly parallel computing at the expense of operating frequency. Recently, multiple manufacturers have started working on specialized deep learning hardware. Most of the time, GPUs are a good choice for deep learning models or other parallelizable algorithms such as XGboost gradient-boosted trees. However, not all applications benefit equally.

In natural language processing (NLP), for instance, batch sizes often need to be small, so the parallelization of operations does not work as well since not that many samples are processed at the same time. Additionally, some words...