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
Other Books You May Enjoy

The modularity tradeoff

This chapter has shown that it is possible, and often useful, to aid a machine learning model with some rule-based system. You might also have noticed that the images in the dataset were all cropped to show only one plant.

While we could have built a model to locate and classify the plants for us, in addition to classifying it, we could have also built a system that would output the treatment a plant should directly receive. This begs the question of how modular we should make our systems.

End-to-end deep learning was all the rage for several years. If given a huge amount of data, a deep learning model can learn what would otherwise have taken a system with many components much longer to learn. However, end-to-end deep learning does have several drawbacks:

  • End-to-end deep learning needs huge amounts of data. Because models have so many parameters, a large amount of data is needed in order to avoid overfitting.

  • End-to-end deep learning is hard to debug. If you replace...