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Machine Learning for Finance

You're reading from Machine Learning for Finance Principles and practice for financial insiders

Product type Paperback

Published in May 2019

Publisher Packt

ISBN-13 9781789136364

Length 456 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Authors (2):

Jannes Klaas

James Le

View More author details

Table of Contents (15) Chapters

Machine Learning for Finance

Contributors

Preface

Other Books You May Enjoy

1. Neural Networks and Gradient-Based Optimization

2. Applying Machine Learning to Structured Data FREE CHAPTER

3. Utilizing Computer Vision

4. Understanding Time Series

5. Parsing Textual Data with Natural Language Processing

6. Using Generative Models

7. Reinforcement Learning for Financial Markets

8. Privacy, Debugging, and Launching Your Products

9. Fighting Bias

10. Bayesian Inference and Probabilistic Programming

Index

E2E modeling

Our current approach relies on engineered features. As we discussed at the start of this chapter, an alternative method is E2E modeling. In E2E modeling, both raw and unstructured data about a transaction is used. This could include the description text of a transfer, video feeds from cameras monitoring a cash machine, or other sources of data. E2E is often more successful than feature engineering, provided that you have enough data available.

To get valid results, and to successfully train the data with an E2E model it can take millions of examples. Yet, often this is the only way to gain an acceptable result, especially when it is hard to codify the rules for something. Humans can recognize things in images well, but it is hard to come up with exact rules that distinguish things, which is where E2E shines.

In the dataset used for this chapter, we do not have access to more data, but the rest of the chapters of this book demonstrate various E2E models.

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Machine Learning for Finance

Published in: May 2019

Publisher: Packt

ISBN-13: 9781789136364

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Authors (2)

James Le

James Le

James Le is currently a master's student studying Computer Science at Rochester Institute of Technology. He is pursuing a research path in Computer Vision using Deep Learning. Moreover, he is an active freelance data scientist and data journalist, with specialties including machine/deep learning, recommendation systems, and data analysis/visualization.

See other products by James Le

Jannes Klaas

Jannes Klaas

Jannes Klaas is a quantitative researcher with a background in economics and finance. He taught machine learning for finance as lead developer for machine learning at the Turing Society, Rotterdam. He has led machine learning bootcamps and worked with financial companies on data-driven applications and trading strategies. Jannes is currently a graduate student at Oxford University with active research interests including systemic risk and large-scale automated knowledge discovery.

See other products by Jannes Klaas

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