Are there any particular references that are recommended for learning about AI / deep learning and how to apply them to quant finance?

I have written programs that price options, done sentiment analysis, SVM etc (to give an indication of my programming and mathematical background).



My all-round favourite for ML-in-Finance: "Advances in Financial Machine Learning" by M. de Prado (https://www.wiley.com/en-hk/Advances+in+Financial+Machine+Learning-p-9781119482086)

He doesn't spend so much time dealing with specific ML models, but talks about the unique challenges faced by financial data scientists and the ways to handle data, conduct strategy research and backtesting, and the style and techniques (labelling, bagging, metamodels...) to apply generic ML techniques in a financial setting.

M De Prado - FML

My favourite introduction to Deep Learning: Hands-On Machine Learning with SciKit-Learn and TensorFlow

Intensive introduction to Neural Nets via Keras (the author created this library) and Tensorflow, with lots of Python code examples

Geron - Tensorflow-Keras

My favourite pure-programming for Data Science-type problems: "Scala for Data Science" by P Bugnion

An immersive introduction to Scala with a focus on Data Science problems, and a focus on how to move products into production safely

P Bugnion - Scala Data Science

  • $\begingroup$ Thanks, I will take a look. Are there any books you recommend for learning the programming and mathematics for AI, for more advanced topics? $\endgroup$ – PythonLearner Sep 25 '20 at 12:03
  • $\begingroup$ Expanding the above with a couple of extra recommendations $\endgroup$ – StackG Sep 25 '20 at 13:12
  • $\begingroup$ The Geron book is very good as it takes you through Scikit-learn in a lot of detail and then does Tensor flow which becomes much more advanced. $\endgroup$ – Dom Sep 25 '20 at 13:30
  • $\begingroup$ Thanks all. Think I will start with the Geron book! $\endgroup$ – PythonLearner Sep 25 '20 at 13:43

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