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  • 1
    UID:
    (DE-627)1737234750
    Format: xxiv, 238 Seiten , Illustrationen
    ISBN: 9781786349361 , 9781786349644
    Series Statement: Advanced textbooks in mathematics
    Content: "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"
    Note: Includes bibliographical references and index
    Additional Edition: 9781786349378
    Additional Edition: 9781786349385
    Additional Edition: Erscheint auch als Online-Ausgabe Ni, Hao (Lecturer in mathematics) Introduction to machine learning in quantitative finance New Jersey : World Scientific, [2021]
    Additional Edition: Erscheint auch als Online-Ausgabe Ni, Hao An introduction to machine learning in quantitative finance New Jersey : World Scientific, 2021 9781786349378
    Additional Edition: 9781786349385
    Additional Edition: Erscheint auch als Online-Ausgabe Ni, Hao An introduction to machine learning in quantitative finance New Jersey : World Scientific, 2021 9781786349378
    Additional Edition: 9781786349385
    Language: English
    Keywords: Lehrbuch
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    UID:
    (DE-605)HT030710211
    Format: 1 Online-Ressource (263 Seiten)
    ISBN: 9781786349378
    Series Statement: Advanced Textbooks In Mathematics
    Additional Edition: Erscheint auch als Druck-Ausgabe 9781786349361
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 3
    UID:
    (DE-627)1819942600
    Format: 1 Online-Ressource ( xxiv, 238 pages) , Illustrationen
    ISBN: 9781786349378 , 9781786349385
    Series Statement: Advanced textbooks in mathematics
    Content: Intro -- Contents -- Preface -- About the Authors -- Acknowledgments -- Disclaimer -- Listings -- 1. Overview of Machine Learning and Financial Applications -- 1.1 Big Data Era -- 1.2 Machine Learning -- 1.3 Quantitative Finance -- 1.3.1 Challenges of financial data -- 1.3.2 Recent development of machine learning for financial applications -- 1.3.3 The future of quantitative finance -- 1.4 Next Generation of Talents in Quantitative Finance -- 1.5 Outline of the Book -- 1.6 Useful Resources -- 1.6.1 Python libraries -- 1.6.2 Books and other online reading materials -- 1.7 Go Beyond This Book -- 2. Supervised Learning -- 2.1 Framework of Regression -- 2.1.1 Model -- 2.1.2 Loss function -- 2.1.3 Optimization -- 2.1.3.1 Gradient descent method -- 2.1.3.2 Discussion on learning rate -- 2.1.3.3 Batch gradient descent -- 2.1.3.4 Stochastic gradient descent -- 2.1.3.5 Mini-batch gradient descent -- 2.1.3.6 Comparison of three types of gradient descent -- 2.1.4 Prediction and validation -- 2.1.4.1 Statistics-based approach -- 2.1.4.2 Machine learning-based approach -- 2.1.4.3 Cross-validation and parameter tuning -- 2.2 From Regression to Classification -- 2.2.1 Categorical output -- 2.2.2 Model -- 2.2.3 Loss function and optimization -- 2.2.4 Prediction and validation -- 2.2.4.1 Accuracy -- 2.2.4.2 Confusion matrix -- 2.2.4.3 Other metrics for binary classification -- 2.2.4.4 Numerical example -- 2.3 Model Ensemble -- 2.3.1 Intuition of ensemble -- 2.3.2 Homogeneous weak learners ensemble -- 2.3.3 Heterogeneous weak learners ensemble -- 2.4 Exercises -- 3. Linear Regression and Regularization -- 3.1 Ordinary Least Squares Method -- 3.1.1 Derivation -- 3.1.2 Pros and cons -- 3.2 Linear Model with Regularization -- 3.2.1 Regularization -- 3.2.2 Ridge Regression -- 3.2.3 Lasso Regression -- 3.2.4 Numerical example.
    Note: Description based on publisher supplied metadata and other sources
    Additional Edition: 9781786349361
    Additional Edition: 9781786349644
    Additional Edition: Erscheint auch als Druck-Ausgabe Ni, Hao An introduction to machine learning in quantitative finance New Jersey : World Scientific, 2021 9781786349361
    Additional Edition: 9781786349644
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 4
    UID:
    (DE-627)1820648664
    Format: 1 Online-Ressource (xxiv, 238 Seiten) , Illustrationen
    ISBN: 9781786349378 , 9781786349385
    Series Statement: Advanced textbooks in mathematics
    Content: "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"
    Note: Includes bibliographical references and index
    Additional Edition: 9781786349361
    Additional Edition: 9781786349644
    Additional Edition: Erscheint auch als Druck-Ausgabe Ni, Hao An introduction to machine learning in quantitative finance New Jersey : World Scientific, 2021 9781786349361
    Additional Edition: 9781786349644
    Language: English
    Keywords: Lehrbuch
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 5
    UID:
    (DE-603)473294087
    Format: xxiv, 238 Seiten , Illustrationen, Diagramme
    ISBN: 9781786349361
    Series Statement: Advanced textbooks in mathematics
    Note: Literaturverzeichnis Seite 231-234
    Additional Edition: 9781786349644
    Additional Edition: 9781786349378
    Additional Edition: 9781786349385
    Language: English
    Subjects: Economics
    RVK:
    Library Location Call Number Volume/Issue/Year Availability
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  • 6
    UID:
    (DE-604)BV047116098
    Format: xxiv, 238 Seiten , Illustrationen, Diagramme
    ISBN: 9781786349361 , 9781786349644
    Series Statement: Advanced textbooks in mathematics
    Note: Includes bibliographical references and index
    Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-1-78634-937-8
    Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-1-78634-938-5
    Language: English
    Subjects: Computer Science , Economics
    RVK:
    RVK:
    Keywords: Finanzwirtschaft ; Maschinelles Lernen ; Mathematische Modellierung
    Library Location Call Number Volume/Issue/Year Availability
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  • 7
    UID:
    (DE-604)BV048931774
    Format: 1 Online-Ressource (xxiv, 238 Seiten) , Illustrationen, Diagramme
    ISBN: 9781786349385
    Series Statement: Advanced textbooks in mathematics
    Note: Includes bibliographical references and index
    Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-1-78634-937-8
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-1-78634-964-4
    Language: English
    Subjects: Computer Science , Economics
    RVK:
    RVK:
    Keywords: Finanzwirtschaft ; Maschinelles Lernen ; Mathematische Modellierung
    URL: Volltext  (URL des Erstveröffentlichers)
    Library Location Call Number Volume/Issue/Year Availability
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  • 8
    UID:
    (DE-605)HT021000836
    Format: xxiv, 238 Seiten , Illustrationen, Diagramme
    ISBN: 9781786349361 , 9781786349644
    Series Statement: Advanced textbooks in mathematics
    Additional Edition: Erscheint auch als Online-Ausgabe, ebook 9781786349378
    Additional Edition: Erscheint auch als Online-Ausgabe, ebook other 9781786349385
    Language: English
    Subjects: Computer Science , Economics
    RVK:
    RVK:
    Keywords: Finanzwirtschaft ; Maschinelles Lernen ; Mathematische Modellierung
    Library Location Call Number Volume/Issue/Year Availability
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  • 9
    UID:
    (DE-602)b3kat_BV047116098
    Format: xxiv, 238 Seiten , Illustrationen, Diagramme
    ISBN: 9781786349361 , 9781786349644
    Series Statement: Advanced textbooks in mathematics
    Note: Includes bibliographical references and index
    Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-1-78634-937-8
    Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-1-78634-938-5
    Language: English
    Subjects: Computer Science , Economics
    RVK:
    RVK:
    Keywords: Finanzwirtschaft ; Maschinelles Lernen ; Mathematische Modellierung
    Library Location Call Number Volume/Issue/Year Availability
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  • 10
    UID:
    (DE-101)1276425082
    Format: Online-Ressource
    ISSN: 1521-3765
    Content: Abstract: Targeting amyloid‐β (Aβ)‐induced complex neurotoxicity has received considerable attention in the therapeutic and preventive treatment of Alzheimer’s disease (AD). The complex pathogenesis of AD suggests that it requires comprehensive treatment, and drugs with multiple functions against AD are more desirable. Herein, AuNPs@POMD‐pep (AuNPs: gold nanoparticles, POMD: polyoxometalate with Wells–Dawson structure, pep: peptide) were designed as a novel multifunctional Aβ inhibitor. AuNPs@POMD‐pep shows synergistic effects in inhibiting Aβ aggregation, dissociating Aβ fibrils and decreasing Aβ‐mediated peroxidase activity and Aβ‐induced cytotoxicity. By taking advantage of AuNPs as vehicles that can cross the blood–brain barrier (BBB), AuNPs@POMD‐pep can cross the BBB and thus overcome the drawbacks of small‐molecule anti‐AD drugs. Thus, this work provides new insights into the design and synthesis of inorganic nanoparticles as multifunctional therapeutic agents for treatment of AD.
    In: volume:21
    In: number:2
    In: year:2015
    In: pages:829-835
    In: extent:7
    In: Chemistry - a European journal, Weinheim : Wiley-VCH, 1995-, 21, Heft 2 (2015), 829-835 (gesamt 7), 1521-3765
    Language: English
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