Machine Learning for Financial Risk Management with Python

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Chapter 1 Health PoliciesChapter 2 Health IndicatorsChapter 3 Health Promotion and ProtectionChapter 4 Control of Communicable DiseasesChapter 5 Management of Pharmaceuticals and FoodsChapter 6 Medica

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International Financial Management provides students with a foundation for analysis through a text that is well-organized, comprehensive, and provides up-to-date coverage of the topics. Like the first nine editions, it is written based on two tenets: emphasis on the basics and emphasis on a managerial perspective.The scope and content of international finance has been fast evolving due to cycles of deregulations and regulations of financial markets, product innovations, and technological advancements. As capital markets of the world are becoming more integrated, a solid understanding of international finance has become essential for astute corporate decision making. Reflecting the growing importance of international finance as a discipline, we have seen a sharp increase in the demand for experts in the area in both the corporate and academic worlds.International Financial Management discussion is written so that a self-contained treatment of each subject is presented in a user-friendly

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A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory.This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the ne

This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely “from scratch” based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.

This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely “from scratch” based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.

This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. In addition to describing the mathematical principles of the topic, and its historical evolution, strong connections are drawn with underlying methods from statistical physics and current applications within science and engineering. Closely based around a well-established undergraduate course, this pedagogical text provides a solid understanding of the key aspects of modern machine learning with artificial neural networks, for students in physics, mathematics, and engineering. Numerous exercises expand and reinforce key concepts within the book and allow students to hone their programming skills. Frequent references to current research develop a detailed perspective on the state-of-the-art in machine learning research.

Redja's Principles of Risk Management and Insurance provides an in-depth examination of major risk themes. Using rich and up-to-date content on the basic concepts of risk and insurance, and introductory and advanced topics in traditional and enterprise risk management, the text is relevant to a wide number of disciplines in the business realm. Fully updated and revised, the 14th Edition covers global topics ranging from natural disasters and terrorism, to domestic issues like the ever-evolving Affordable CareAct and healthcare reform. Principles sets itself apart by placing primary emphasis on insurance consumers and blends basic risk management and insurance principles with consumer considerations, allowing students to apply basic concepts to their own personal risk management and insurance programs.

Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure.Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value.

Python機器學習第三版（下） Python Machine Learning - Third Edition 第三版－最新修訂版，新增TensorFlow 2、GAN和強化學習等實用內容 使用Python的scikit-learn和TensorFlow 2融會貫通機器學習與深度學習 循序漸進、由淺入深，好評熱銷再進化！最新修訂的《Python機器學習第三版》是一本不容錯過的全方位指南，也是讀者

《Python機器學習》面向機器學習新手，主要內容如下： ● Python機器學習的一些基本庫，包括NumPy、Pandas和matplotlib庫 ● 常見的機器學習算法，包括回歸、聚類、分類和異常檢測 ● 使用Python和Scikit-learn庫進行機器學習 ● 將機器學習模型部署為Web服務 ● 使用Microsoft Azure Machine Learning Studio進行機器學

Start with a Solid Foundation to Secure Your CISSP!The Effective CISSP: Security and Risk Management is for CISSP aspirants and those who are interested in information security or confused by cybersec

The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning.

Put the power of AWS Cloud machine learning services to work in your business and commercial applications! Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabiliti

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Corporate Risk Management focuses on the corporate finance dimension of risk management, presenting several theoretical models that justify risk management in different industries, and empirically ver

Transform your approach to oprisk modelling with a proven, non-statistical methodology Operational Risk Modeling in Financial Services provides risk professionals with a forward-looking approach to ri

First published in 2000, Risk Management is a two volume set, comprised of the most significant and influential articles by the leading authorities in the studies of risk management. The volumes inclu

Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you’ll see examples that demonst