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Podcasts
Gergely Daróczi, Michael Puhle, Edina Berlinger (EURO), Daniel Daniel Havran, ...
Introduction to R for Quantitative Finance will show you how to solve real-world quantitative fi nance problems using the statistical computing language R. The book covers diverse topics ranging from time series analysis to fi nancial networks. Each chapter briefl y presents the theory behind specific concepts and deals with solving a diverse range of problems using R with the help of practical examples.This book will be your guide on how to use and master R in order to solve quantitative finance problems. This book covers the essentials of quantitative finance, taking you through a number of clear and practical examples in R that will not only help you to understand the theory, but how to effectively deal with your own real-life problems.Starting with time series analysis, you will also learn how to optimize portfolios and how asset pricing models work. The book then covers fixed income securities and derivatives such as credit risk management.
Matthew Nohr, Isaac Rabinovitch
AppFog is the leading platform-as-a-service provider of PHP, Ruby, Node.js, and Java solutions. It is used by developers worldwide to deploy tens of thousands of applications. AppFog delivers a reliable, scalable, and fast platform for deploying applications in the cloud.This book is a hands-on guide that will walk you through creating and deploying applications to the cloud using AppFog, which will allow you to get your application deployed without the hassle of setting up servers.This book demonstrates how to use the AppFog service to build an application and have it running in the Cloud. It will walk you through the initial AppFog setup process and explain how to create your first application in minutes.You will also discover how to use services such as databases to make your applications more powerful. You will also learn how to create applications from scratch.You will find out everything you need to know to get an application running in the cloud for the first time.
Mykyta Chernenko, Artem Korchunov
This book takes you on a journey with TypeScript 5, introducing its nuances and advantages over traditional JavaScript, explaining TypeScript basics, and highlighting the syntax, typing, generics, classes, and interfaces. Expert advice will help you build your understanding of TypeScript's role in modern web applications. You'll gain hands-on experience with the tools required to write full-stack web applications end to end, beginning with setting up the Bun environment for backend development. This book also explores server-side functionality such as authentication, routing, and database integration with PostgreSQL. The second part of the book focuses on the integration of the OpenAI API, demonstrating the use of the GPT API for different use cases. The final part covers frontend development using Svelte, guiding you through UI design, state management, and performance optimization. Each chapter has real-world examples and comparisons to other technologies, providing you with a comprehensive understanding of full-stack development.By the end of this book, you'll have learned how to build modern web applications using TypeScript 5 and confidently tackle full-stack development challenges.
Most programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models.You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you’ll explore CNN, recurrent neural network (RNN), and GAN models and their application.By the end of this book, you'll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL.