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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GANs work a lot like an art forger and a museum curator. Every day, the art forger tries to sell some fake art to the museum, and every day the curator tries to distinguish whether a certain piece is real or fake. The forger learns from their failures. By trying to fool the curator and observing what leads to success and failure, they become a better forger. But the curator learns too. By trying to stay ahead of the forger, they become a better curator. As time passes, the forgeries become better and so does the distinguishing process. After years of battle, the art forger is an expert that can draw just as well as Picasso and the curator is an expert that can distinguish a real painting by tiny details.

Technically, a GAN consists of two neural networks: a generator, which produces data from a random latent vector, and a discriminator, which classifies data as "real," that is, stemming from the training set, or "fake," that is, stemming from the generator.

We can visualize a GAN scheme...