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Machine Learning in Finance: From Theory to Practice
Chapter 1. Introduction.- Chapter 2. Probabilistic Modeling.- Chapter 3. Bayesian Regression & Gaussian Processes.- Chapter 4. Feed Forward Neural Networks.- Chapter 5. Interpretability.- Chapter 6. Sequence Modeling.- Chapter 7. Probabilistic Sequence Modeling.- Chapter 8. Advanced Neural Networks.- Chapter 9. Introduction to Reinforcement learning.- Chapter 10. Applications of Reinforcement Learning.- Chapter 11. Inverse Reinforcement Learning and Imitation Learning.- Chapter 12. Frontiers of Machine Learning and Finance.
576 pages, Paperback
Published July 3, 2020
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October 6, 2022
Really surprised by how good this book is. I know nothing about finance so don't know how good this is in that aspect, but as a cute machine learning overview book with a finance flavor, this is very nice. It's more on practice than theory, though it does kind of explain some heuristics to aid the understanding. That being said it's not as dense as some others, say for instance Fan's Statistical Foundation of Data Science, but still dense enough to provide useful insights into some modern ML methods.
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