Machine Learning for Financial Risk Management with Python

Augment your knowledge of Python with this entertaining learning guide, which features 100 exercises and programming puzzles and solutions. Python Challenges will help prepare you for your next exam or a job interview, and covers numerous practical topics such as strings, data structures, recursion, arrays, and more.Each topic is addressed in its own separate chapter, starting with an introduction to the basics and followed by 10 to 15 exercises of various degrees of difficulty, helping you to improve your programming skills effectively. Detailed sample solutions, including the algorithms used for all tasks, are included to maximize your understanding of each area. Author Michael Inden also describes alternative solutions and analyzes possible pitfalls and typical errors.Three appendices round out the book: the first covers the Python command line interpreter, which is often helpful for trying out the code snippets and examples in the book, followed by an overview of Pytest for unit te

Take your Python machine learning ideas and create serverless web applications accessible by anyone with an Internet connection. Some of the most popular serverless cloud providers are covered in this

Get started with Julia for engineering and numerical computing, especially data science, machine learning, and scientific computing applications. This book explains how Julia provides the functionalit

This comprehensive reference will help programmers transition into PostgreSQL for the first time, while learning about key relational database design and management principles simultaneously, to help

From the elegance of the Black-Scholes equation to the complexity of multi-factor interest rate models and hybrid derivatives, this book is your comprehensive guide to quantitative finance, complete with 15+ advanced C++ projects using QuantLib and Boost.You'll move seamlessly from mathematical foundations to real-world implementation, building a professional-grade toolkit for pricing, risk analysis, and calibration. Inside, you will learn core option pricing methods, master single-and multi-factor interest rate models, and construct and calibrate trees and lattices for advanced derivatives. You will also explore cutting edge products: exotic multi-asset options, hybrid derivatives, credit instruments, and cross-currency swaps.Packed with practical source code, step-by-step calibrations, and performance-tuned Boost integration, this book bridges the gap between academic finance and production-grade quant development. Whether you're a quant developer, financial engineer, or an advanced

With the economic crisis that began in 2008, a long-standing trend toward increased regulation is becoming a flood. The clamor for improved enterprise risk management and the complexity of multination

Implement real-time data processing applications on the Raspberry Pi. This book uniquely helps you work with data science concepts as part of real-time applications using the Raspberry Pi as a localized cloud. You'll start with a brief introduction to data science followed by a dedicated look at the fundamental concepts of Python programming. Here you'll install the software needed for Python programming on the Pi, and then review the various data types and modules available. The next steps are to set up your Pis for gathering real-time data and incorporate the basic operations of data science related to real-time applications. You'll then combine all these new skills to work with machine learning concepts that will enable your Raspberry Pi to learn from the data it gathers. Case studies round out the book to give you an idea of the range of domains where these concepts can be applied. By the end of Data Science with the Raspberry Pi, you'll understand that many applications are now de