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 a number of practical tips for debugging and improving your model. Let's recap all of the things that we have looked at:

  • Finding flaws in your data that lead to flaws in your learned model

  • Using creative tricks to make your model learn more from less data

  • Unit testing data in production or training to make sure standards are met

  • Being mindful of privacy

  • Preparing data for training and avoiding common pitfalls

  • Inspecting the model and peering into the "black box"

  • Finding optimal hyperparameters

  • Scheduling learning rates in order to reduce overfitting

  • Monitoring training progress with TensorBoard

  • Deploying machine learning products and iterating on them

  • Speeding up training and inference

You now have a substantial number of tools in your toolbox that will help you run actual, practical machine learning projects and deploy them in real-life (for example, trading) applications.

Making sure your model works before deploying it is crucial and failure to properly scrutinize...