Machine Learning for Financial Risk Management with Python

風險建模演算法 「Abdullah Karasan成功展現了在金融風險管理領域中使用機器學習的能力，這是對任何金融機構都攸關重要的功能。」 ―Yves J. Hilpisch博士 The Python Quants與The AI Machine創辦人及總裁 「如果您需要將統計和機器學習方法應用在金融風險分析的入門指南，那麼這是一個很好的起點。」 ―Graham L. Giller 《Adventures in Financial Data Science》作者 金融風險管理在人工智慧的幫助下發展迅速。透過這本實用指南，開發人員、程式設計師、工程師、金融分析師、風險分析師及定量和演算法分析師，將可以機器學習和深度學習模型進行金融風險評估。建立基於人工智慧的財務建模實務技能後，您將學習要如何運用機器學習模型來取代傳統的金融風險模型。 作者Abdullah Karasan幫助您探索金融風險建模背後的理論，再深入研究使用Python運用機器學習模型以對金融風險進行建模的實際方法。 有了這本書，您將可以： ‧回顧經典的時間序列應用並將其與深度學習模型進行比較 ‧使用支撐向量迴歸、神經網路和深度學習來探索波動率模型以衡量風險程度 ‧使用機器學習技術來改善市場風險模型(VaR和ES)，並包括了流動性維度 ‧使用分群和貝氏方法來進行信用風險分析 ‧使用高斯混合模型和關聯結構模型來捕捉流動性風險的不同面向 ‧使用機器學習模型來進行詐欺偵測 ‧使用機器學習模型來預測股價崩盤並識別其決定因素

Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design mod

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.

Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a platform developed by Facebook's Artificial Intelligence Research Group.You'll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, you'll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms. You'll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models