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Machine Learning for Finance

You're reading from   Machine Learning for Finance Principles and practice for financial insiders

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781789136364
Length 456 pages
Edition 1st Edition
Languages
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Authors (2):
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Jannes Klaas Jannes Klaas
Author Profile Icon Jannes Klaas
Jannes Klaas
James Le James Le
Author Profile Icon James Le
James Le
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Toc

Table of Contents (15) Chapters Close

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

Farewell


And thus, we close the last chapter of our journey, and I say goodbye to you, dear reader. Let's look back at the table of contents that we were met with at the start of our journey.

Over the past 10 chapters, we've covered a whole lot, including the following:

  • Gradient descent-based optimization

  • Feature engineering

  • Tree-based methods

  • Computer vision

  • Time series models

  • Natural language processing

  • Generative models

  • Debugging machine learning systems

  • Ethics in machine learning

  • Bayesian inference

In each chapter, we created a large bag of practical tips and tricks that you can use. This will allow you to build state-of-the-art systems that will change the financial industry.

Yet, in many ways we have only scratched the surface. Each of the chapter topics merit their own book, and even that would not adequately cover everything that could be said about machine learning in finance.

I leave you with this thought: Machine learning in finance is an exciting field in which there is still much to uncover...

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