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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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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

Deep Learning for Finance

Deep learning represents the very cutting edge of Artificial Intelligence (AI). Unlike machine learning, deep learning takes a different approach in making predictions by using a neural network. An artificial neural network is modeled on the human nervous system, consisting of an input layer and an output layer, with one or more hidden layers in between. Each layer consists of artificial neurons working in parallel and passing outputs to the next layer as inputs. The word deep in deep learning comes from the notion that as data passes through more hidden layers in an artificial neural network, more complex features can be extracted.

TensorFlow is an open source, powerful machine learning and deep learning framework developed by Google. In this chapter, we will take a hands-on approach to learning TensorFlow by building a deep learning model with four...

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