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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

Convolutional Neural Networks

Convolutional Neural Networks, ConvNets, or CNNs for short, are the driving engine behind computer vision. ConvNets allow us to work with larger images while still keeping the network at a reasonable size.

The name Convolutional Neural Network comes from the mathematical operation that differentiates them from regular neural networks. Convolution is the mathematically correct term for sliding one matrix over another matrix. We'll explore in the next section, Filters on MNIST, why this is important for ConvNets, but also why this is not the best name in the world for them, and why ConvNets should, in reality, be called filter nets.

You may be asking, "but why filter nets?" The answer is simply because what makes them work is the fact that they use filters.

In the next section, we will be working with the MNIST dataset, which is a collection of handwritten digits that has become a standard "Hello, World!" application for computer vision.

Filters on MNIST

What does...

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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.

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