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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

Document similarity with word embeddings

The practical use case of word vectors is to compare the semantic similarity between documents. If you are a retail bank, insurance company, or any other company that sells to end users, you will have to deal with support requests. You'll often find that many customers have similar requests, so by finding out how similar texts are semantically, previous answers to similar requests can be reused, and your organization's overall service can be improved.

spaCy has a built-in function to measure the similarity between two sentences. It also comes with pretrained vectors from the Word2Vec model, which is similar to GloVe. This method works by averaging the embedding vectors of all the words in a text and then measuring the cosine of the angle between the average vectors. Two vectors pointing in roughly the same direction will have a high similarity score, whereas vectors pointing in different directions will have a low similarity score. This is visualized...

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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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