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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Now we're at the end of the chapter, why not try some of the following exercises? You'll find guides on how to complete them all throughout this chapter:

  • A good trick is to use LSTMs on top of one-dimensional convolution, as one-dimensional convolution can go over large sequences while using fewer parameters. Try to implement an architecture that first uses a few convolutional and pooling layers and then a few LSTM layers. Try it out on the web traffic dataset. Then try adding (recurrent) dropout. Can you beat the LSTM model?

  • Add uncertainty to your web traffic forecasts. To do this, remember to run your model with dropout turned on at inference time. You will obtain multiple forecasts for one time step. Think about what this would mean in the context of trading and stock prices.

  • Visit the Kaggle datasets page and search for time series data. Make a forecasting model. This involves feature engineering with autocorrelation and Fourier transformation, picking the right model from the...