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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

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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

The machine learning software stack

In this chapter, we will be using a range of different libraries that are commonly used in machine learning. Let's take a minute to look at our stack, which consists of the following software:

Keras: A neural network library that can act as a simplified interface to TensorFlow.
NumPy: Adds support for large, multidimensional arrays as well as an extensive collection of mathematical functions.
Pandas: A library for data manipulation and analysis. It's similar to Microsoft's Excel but in Python, as it offers data structures to handle tables and the tools to manipulate them.
Scikit-learn: A machine learning library offering a wide range of algorithms and utilities.
TensorFlow: A dataflow programming library that facilitates working with neural networks.
Matplotlib: A plotting library.
Jupyter: A development environment. All of the code examples in this book are available in Jupyter Notebooks.

The majority of this book is dedicated to working with the Keras...

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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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