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 that we're at the end of the chapter, let's see what we've learned. To finish this chapter, I've included three exercises that will challenge you based on what we've covered in this chapter:

  1. Add an extra layer to the encoder of the translation model. The translation model might work better if it had a bit more capacity to learn the structure of French sentences. Adding one more LSTM layer will be a good exercise to learn about the functional API.

  2. Add attention to the encoder of the translation model. Attention will allow the model to focus on the (English) words that really matter for translation. It is best to use attention as the last layer. This task is a bit harder than the previous one, but you will understand the inner workings of attention much better.

  3. Visit Daily News for Stock Market Prediction at The task is to use the daily news as an input to predict stock prices. There are a number of kernels already that can help you with...