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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

Working with big image datasets

Images tend to be big files. In fact, it's likely that you will not be able to fit your entire image dataset into your machine's RAM.

Therefore, we need to load the images from disk "just in time" rather than loading them all in advance. In this section, we will be setting up an image data generator that loads images on the fly.

We'll be using a dataset of plant seedlings in this case. This was provided by Thomas Giselsson and others, 2017, via their publication, A Public Image Database for Benchmark of Plant Seedling Classification Algorithms.

This dataset is available from the following link: https://arxiv.org/abs/1711.05458.

You may be wondering why we're looking at plants; after all, plant classifications are not a common problem that is faced in the finance sector. The simple answer is that this dataset lends itself to demonstrating many common computer vision techniques and is available under an open domain license; it's therefore a great training dataset...

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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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