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Mastering Python for Finance

You're reading from   Mastering Python for Finance Implement advanced state-of-the-art financial statistical applications using Python

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781789346466
Length 426 pages
Edition 2nd Edition
Languages
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Author (1):
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James Ma Weiming James Ma Weiming
Author Profile Icon James Ma Weiming
James Ma Weiming
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Toc

Table of Contents (16) Chapters Close

Preface 1. Section 1: Getting Started with Python
2. Overview of Financial Analysis with Python FREE CHAPTER 3. Section 2: Financial Concepts
4. The Importance of Linearity in Finance 5. Nonlinearity in Finance 6. Numerical Methods for Pricing Options 7. Modeling Interest Rates and Derivatives 8. Statistical Analysis of Time Series Data 9. Section 3: A Hands-On Approach
10. Interactive Financial Analytics with the VIX 11. Building an Algorithmic Trading Platform 12. Implementing a Backtesting System 13. Machine Learning for Finance 14. Deep Learning for Finance 15. Other Books You May Enjoy

A brief introduction to deep learning

The theory behind deep learning began as early as the 1940s. However, its popularity has soared in recent years thanks in part to improvements in computing hardware technology, smarter algorithms, and the adoption of deep learning frameworks. There is much to cover beyond this book. This section serves as a quick guide to gain a working knowledge for following the examples that we will cover in later parts of this chapter.

What is deep learning ?

In Chapter 10, Machine Learning for Finance, we learned how machine learning is useful for making predictions. Supervised learning uses error-minimization techniques to fit a model with training data, and can be regression based or classification...

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