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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

Exercises

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:

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.
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.
Visit Daily News for Stock Market Prediction at https://www.kaggle.com/aaron7sun/stocknews. 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...

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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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