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Titlebook: OCaml Scientific Computing; Functional Programmi Liang Wang,Jianxin Zhao,Richard Mortier Textbook 2022 The Editor(s) (if applicable) and Th

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發(fā)表于 2025-3-21 18:37:57 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)OCaml Scientific Computing
副標(biāo)題Functional Programmi
編輯Liang Wang,Jianxin Zhao,Richard Mortier
視頻videohttp://file.papertrans.cn/701/700010/700010.mp4
概述Shows how the expressiveness of OCaml allows for fast and safe development of data science applications.Exemplifies use cases drawn from many areas of Data Science, Machine Learning, and AI.Explains s
叢書(shū)名稱(chēng)Undergraduate Topics in Computer Science
圖書(shū)封面Titlebook: OCaml Scientific Computing; Functional Programmi Liang Wang,Jianxin Zhao,Richard Mortier Textbook 2022 The Editor(s) (if applicable) and Th
描述.This book is about the harmonious synthesis of functional programming and numerical computation. It shows how the expressiveness of OCaml allows for fast and safe development of data science applications. Step by step, the authors build up to use cases drawn from many areas of Data Science, Machine Learning, and AI, and then delve into how to deploy at scale, using parallel, distributed, and accelerated frameworks to gain all the advantages of cloud computing environments..To this end, the book is divided into three parts, each focusing on a different area. Part I begins by introducing how basic numerical techniques are performed in OCaml, including classical mathematical topics (interpolation and quadrature), statistics, and linear algebra. It moves on from using only scalar values to multi-dimensional arrays, introducing the tensor and Ndarray, core data types in any numerical computing system. It concludes with two more classical numerical computing topics, the solution ofOrdinary Differential Equations (ODEs) and Signal Processing, as well as introducing the visualization module we use throughout this book. Part II is dedicated to advanced optimization techniques that are core
出版日期Textbook 2022
關(guān)鍵詞OCaml; Scientific Computing; Functional Programming; Machine Learning; Numerical Analysis; Programming La
版次1
doihttps://doi.org/10.1007/978-3-030-97645-3
isbn_softcover978-3-030-97644-6
isbn_ebook978-3-030-97645-3Series ISSN 1863-7310 Series E-ISSN 2197-1781
issn_series 1863-7310
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Vector Space Modellingssing (NLP) is a powerful tool for extracting insights from text corpora. NLP is a very large topic with many interesting challenges. In this chapter we focus on information retrieval and specifically ..
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OptimisationOptimisation is one of the most fundamental areas of numerical computing. From simple root finding to advanced machine learning, optimisation is everywhere. In this chapter, we will give you a brief overview of this topic and how the OCaml numerical library, Owl, supports basic optimisation methods.
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Case Study: Neural Style TransferNeural Style Transfer (NST) is an exciting DNN-based application that creates arts. In this chapter we introduce this application in detail: its theory, the implementation, and examples of its use. NST is has been extended in many ways, one of which is the fast style transfer, and we also introduce how this application works with examples.
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