## In Detail F# is a functional programming language that allows you to write simple code for complex problems. Currently, it is most commonly used in the financial sector. Quantitative finance makes ...heavy use of mathematics to model various parts of finance in the real world. If you are interested in using F# for your day-to-day work or research in quantitative finance, this book is a must-have. This book will cover everything you need to know about using functional programming for quantitative finance. Using a functional programming language will enable you to concentrate more on the problem itself rather than implementation details. Tutorials and snippets are summarized into an automated trading system throughout the book. This book will introduce you to F#, using Visual Studio, and provide examples with functional programming and finance combined. The book also covers topics such as downloading, visualizing and calculating statistics from data. F# is a first class programming language for the financial domain. ## Approach The approach is to guide you as a reader from the basics of functional programming and F# to more complex tasks using tutorials and a lot of code examples. As you gain more confidence through out the book, you will be able to modify and write your own code to solve various problems in finance. ## Who this book is for If you are a practitioner of quantitative finance, economics, or mathematics and wish to learn F#, then this book is for you. You may have a basic conceptual understanding of financial concepts and models, but no previous knowledge is expected.
This book takes a single line of code--the extremely concise BASIC program for the Commodore 64 inscribed in the title--and uses it as a lens through which to consider the phenomenon of creative ...computing and the way computer programs exist in culture. The authors of this collaboratively written book treat code not as merely functional but as a text--in the case of 10 PRINT, a text that appeared in many different printed sources--that yields a story about its making, its purpose, its assumptions, and more. They consider randomness and regularity in computing and art, the maze in culture, the popular BASIC programming language, and the highly influential Commodore 64 computer.
This fast-moving tutorial introduces you to OCaml, an industrial-strength programming language designed for expressiveness, safety, and speed. Through the book's many examples, you'll quickly learn ...how OCaml stands out as a tool for writing fast, succinct, and readable systems code using functional programming. Real World OCaml takes you through the concepts of the language at a brisk pace, and then helps you explore the tools and techniques that make OCaml an effective and practical tool. You'll also delve deep into the details of the compiler toolchain and OCaml's simple and efficient runtime system. This second edition brings the book up to date with almost a decade of improvements in the OCaml language and ecosystem, with new chapters covering testing, GADTs, and platform tooling. This title is also available as open access on Cambridge Core, thanks to the support of Tarides. Their generous contribution will bring more people to OCaml.
A practical guide to learning visual perception for self-driving cars for computer vision and autonomous system engineers Key Features * Explore the building blocks of the visual perception system in ...self-driving cars * Identify objects and lanes to define the boundary of driving surfaces using open-source tools like OpenCV and Python * Improve the object detection and classification capabilities of systems with the help of neural networks Book Description The visual perception capabilities of a self-driving car are powered by computer vision. The work relating to self-driving cars can be broadly classified into three components - robotics, computer vision, and machine learning. This book provides existing computer vision engineers and developers with the unique opportunity to be associated with this booming field. You will learn about computer vision, deep learning, and depth perception applied to driverless cars. The book provides a structured and thorough introduction, as making a real self-driving car is a huge cross-functional effort. As you progress, you will cover relevant cases with working code, before going on to understand how to use OpenCV, TensorFlow and Keras to analyze video streaming from car cameras. Later, you will learn how to interpret and make the most of lidars (light detection and ranging) to identify obstacles and localize your position. You'll even be able to tackle core challenges in self-driving cars such as finding lanes, detecting pedestrian and crossing lights, performing semantic segmentation, and writing a PID controller. By the end of this book, you'll be equipped with the skills you need to write code for a self-driving car running in a driverless car simulator, and be able to tackle various challenges faced by autonomous car engineers. What you will learn * Understand how to perform camera calibration * Become well-versed with how lane detection works in self-driving cars using OpenCV * Explore behavioral cloning by self-driving in a video-game simulator * Get to grips with using lidars * Discover how to configure the controls for autonomous vehicles * Use object detection and semantic segmentation to locate lanes, cars, and pedestrians * Write a PID controller to control a self-driving car running in a simulator Who this book is for This book is for software engineers who are interested in learning about technologies that drive the autonomous car revolution. Although basic knowledge of computer vision and Python programming is required, prior knowledge of advanced deep learning and how to use sensors (lidar) is not needed.
Automatic operations of multi-functional and time-lapse live-cell imaging are necessary for the biomedical science community to study active, multi-faceted, and long-term biological phenomena. To ...achieve automatic control, most existing solutions often require the purchase of extra software programs and hardware that rely on the manufacturers' own specifications. However, these software programs are usually non-user-programmable and unaffordable for many laboratories. To address this unmet need, we have developed a novel open-source software program, titled Automatic Multi-functional Integration Program (AMFIP), as a new Java-based and hardware-independent system that provides proven advantages over existing alternatives to the scientific community. Without extra hardware, AMFIP enables the functional synchronization of the muManager software platform, the Nikon NIS-Elements platform, and other 3rd party software to achieve automatic operations of most commercially available microscopy systems, including but not limited to those from Nikon. AMFIP provides a user-friendly and programmable graphical user interface (GUI), opening the door to expanding the customizability for myriad hardware and software systems according to user-specific experimental requirements and environments. To validate the intended purposes of developing AMFIP, we applied it to elucidate the question whether single cells, prior to their full spreading, can sense and respond to a soft solid substrate, and if so, how does the interaction depend on the cell spreading time and the stiffness of the substrate. Using a CRISPR/Cas9-engineered human epithelial Beas2B (B2B) cell line that expresses mNeonGreen2-tagged mechanosensitive Yes-associated protein (YAP), we show that single B2B cells develop distinct substrate-stiffness-dependent YAP expressions within 10 hours at most on the substrate, suggesting that cells are able to sense, distinguish, and respond to mechanical cues prior to the establishment of full cell spreading. In summary, AMFIP provides a reliable, open-source, and cost-free solution that has the validated long-term utility to satisfy the need of automatic imaging operations in the scientific community.
Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines the Bayesian and frequentist methods of conducting data analyses. The book provides the theoretical background in ...an easy-to-understand approach, encouraging readers to examine the processes that generated their data. Including discussions of model selection, model checking, and multi-model inference, the book also uses effect plots that allow a natural interpretation of data. Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN introduces Bayesian software, using R for the simple modes, and flexible Bayesian software (BUGS and Stan) for the more complicated ones. Guiding the ready from easy toward more complex (real) data analyses ina step-by-step manner, the book presents problems and solutions-including all R codes-that are most often applicable to other data and questions, making it an invaluable resource for analyzing a variety of data types.
Data Parallel C++ Reinders, James; Ashbaugh, Ben; Brodman, James ...
2023.
eBook
This open access book enables C++ programmers to be at the forefront of this exciting and important development that is helping to push computing to new levels. --
