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 this chapter, you have learned about the two most important types of generative models: autoencoders and GANs. We first developed an autoencoder for MNIST images. We then used a similar architecture to encode credit card data and detect fraud. Afterward, we expanded the autoencoder to a VAE. This allowed us to learn distributions of encodings and generate new data that we could use for training.

Afterward, we learned about GANs, again first in the context of MNIST images and then in the context of credit card fraud. We used an SGAN to reduce the amount of data we needed to train our fraud detector. We used model outputs to reduce the amount of labeling necessary through active learning and smarter labeling interfaces.

We've also discussed and learned about latent spaces and the use they have for financial analysis. We saw the t-SNE algorithm and how it can be used to visualize higher dimensional (latent) data. You also got a first impression of how machine learning can solve game...