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An Introduction to Machine Learning in Quantitative Finance aims to demystify ML by uncovering its underlying mathematics and showing how to apply ML methods to real-world financial data. In this book the authorsProvide a systematic and rigorous introduction to supervised, unsupervised and reinforcement learning by establishing essential definitions and theorems.
Dive into various types of neural networks, including artificial nets, convolutional nets, recurrent nets and recurrent reinforcement learning.
Summarize key contents of each section in the tables as a cheat sheet.
Include ample examples of financial applications.
Showcase how to tackle an exemplar ML project on financial data end-to-end.
Provide a GitHub repository [...] that contains supplementary Python codes of all methods/examples.
Featured with the balance of mathematical theorems and practical code examples of ML, this book will help you acquire an in-depth understanding of ML algorithms as well as hands-on experience. After reading An Introduction to Machine Learning in Quantitative Finance, ML tools will not be a black box to you anymore, and you will feel confident in successfully applying what you have learnt to empirical financial data!
An Introduction to Machine Learning in Quantitative Finance aims to demystify ML by uncovering its underlying mathematics and showing how to apply ML methods to real-world financial data. In this book the authorsProvide a systematic and rigorous introduction to supervised, unsupervised and reinforcement learning by establishing essential definitions and theorems.
Dive into various types of neural networks, including artificial nets, convolutional nets, recurrent nets and recurrent reinforcement learning.
Summarize key contents of each section in the tables as a cheat sheet.
Include ample examples of financial applications.
Showcase how to tackle an exemplar ML project on financial data end-to-end.
Provide a GitHub repository [...] that contains supplementary Python codes of all methods/examples.
Featured with the balance of mathematical theorems and practical code examples of ML, this book will help you acquire an in-depth understanding of ML algorithms as well as hands-on experience. After reading An Introduction to Machine Learning in Quantitative Finance, ML tools will not be a black box to you anymore, and you will feel confident in successfully applying what you have learnt to empirical financial data!
| Erscheinungsjahr: | 2021 |
|---|---|
| Fachbereich: | Allgemeines |
| Genre: | Wirtschaft |
| Rubrik: | Recht & Wirtschaft |
| Medium: | Taschenbuch |
| Seiten: | 264 |
| Inhalt: | Kartoniert / Broschiert |
| ISBN-13: | 9781786349644 |
| ISBN-10: | 1786349647 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: | Hao Ni, Xin Dong Jinsong Zheng & Guangx |
| Hersteller: | WSPC (Europe) |
| Maße: | 229 x 152 x 15 mm |
| Von/Mit: | Xin Dong Jinsong Zheng & Guangx Hao Ni |
| Erscheinungsdatum: | 26.03.2021 |
| Gewicht: | 0,388 kg |
| Erscheinungsjahr: | 2021 |
|---|---|
| Fachbereich: | Allgemeines |
| Genre: | Wirtschaft |
| Rubrik: | Recht & Wirtschaft |
| Medium: | Taschenbuch |
| Seiten: | 264 |
| Inhalt: | Kartoniert / Broschiert |
| ISBN-13: | 9781786349644 |
| ISBN-10: | 1786349647 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: | Hao Ni, Xin Dong Jinsong Zheng & Guangx |
| Hersteller: | WSPC (Europe) |
| Maße: | 229 x 152 x 15 mm |
| Von/Mit: | Xin Dong Jinsong Zheng & Guangx Hao Ni |
| Erscheinungsdatum: | 26.03.2021 |
| Gewicht: | 0,388 kg |
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