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An introduction to machine learning ...

Ni, Hao.

An introduction to machine learning in quantitative finance /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	An introduction to machine learning in quantitative finance // Hao Ni ... [et al.].
其他作者:	Ni, Hao.
出版者:	Hackensack, NJ :World Scientific, : c2021.,
面頁冊數:	xxiv, 238 p. :ill. (some col.), ports. ; : 23 cm.;
標題:	Machine learning. -
ISBN:	9781786349361

An introduction to machine learning in quantitative finance /
An introduction to machine learning in quantitative finance /Hao Ni ... [et al.]. - Hackensack, NJ :World Scientific,c2021. - xxiv, 238 p. :ill. (some col.), ports. ;23 cm. - Advanced textbooks in mathematics,2059-769X.

Includes bibliographical references (p. 231-234) and index.

"In today's world, we are increasingly exposed to the words "machine learning" (ML), a term which sounds like a panacea designed to cure all problems ranging from image recognition to machine language translation. Over the past few years, ML has gradually permeated the financial sector, reshaping the landscape of quantitative finance as we know it. 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 authors Provide 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. Supplement Python codes of all the methods/examples in a GitHub repository. 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! The Python codes contained within An Introduction to Machine Learning in Quantitative Finance have been made publicly available on the author's GitHub: https://github.com/deepintomlf/mlfbook.git"--

ISBN: 9781786349361

LCCN: 2020041545Subjects--Topical Terms:

561253
Machine learning.

LC Class. No.: HG106 / .N52 2021

Dewey Class. No.: 332.0285/631

An introduction to machine learning in quantitative finance /
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245 0 3 $a An introduction to machine learning in quantitative finance / $c Hao Ni ... [et al.].
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300 $a xxiv, 238 p. : $b ill. (some col.), ports. ; $c 23 cm.
490 0 $a Advanced textbooks in mathematics, $x 2059-769X
504 $a Includes bibliographical references (p. 231-234) and index.
520 # $a "In today's world, we are increasingly exposed to the words "machine learning" (ML), a term which sounds like a panacea designed to cure all problems ranging from image recognition to machine language translation. Over the past few years, ML has gradually permeated the financial sector, reshaping the landscape of quantitative finance as we know it. 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 authors Provide 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. Supplement Python codes of all the methods/examples in a GitHub repository. 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! The Python codes contained within An Introduction to Machine Learning in Quantitative Finance have been made publicly available on the author's GitHub: https://github.com/deepintomlf/mlfbook.git"-- $c Provided by publisher.
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