OPAC

検索結果一覧に戻る

OCaml Scientific Computing : Functional Programming in Data Science and Artificial Intelligence / by Liang Wang, Jianxin Zhao, Richard Mortier
(Undergraduate Topics in Computer Science. ISSN:21971781)

データ種別	電子ブック
版	1st ed. 2022.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2022
大きさ	XXII, 359 p. 105 illus., 73 illus. in color : online resource
著者標目	*Wang, Liang author Zhao, Jianxin author Mortier, Richard author SpringerLink (Online service)

所蔵情報を非表示

URL	巻　次	配架場所	請求記号	登録番号	コメント	刷　年	状　態	利用注記	ISBN	予約	請求メモ
URL		射水-電子	007	EB0004345	Computer Scinece R0 2005-6,2022-3				9783030976453

書誌詳細を非表示

一般注記	Part I: Numerical Techniques -- 1. Introduction -- 2. Numerical Algorithms -- 3. Statistics -- 4. Linear Algebra -- 5. N-Dimensional Arrays -- 6. Ordinary Differential Equations -- 7. Signal Processing -- Part II: Advanced Data Analysis Techniques -- 8. Algorithmic Differentiation -- 9. Optimisation -- 10. Regression -- 11. Neural Network -- 12. Vector Space Modelling -- Part III: Use Cases -- 13. Case Study: Image Recognition -- 14. Case Study: Instance Segmentation -- 15. Case Study: Neural Style Transfer -- 16. Case Study: Recommender System 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 of Ordinary 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 to most current popular data science fields. We do not focus only on applications but also on the basic building blocks, starting with Algorithmic Differentiation, the most crucial building block that in turn enables Deep Neural Networks. We follow this with chapters on Optimization and Regression, also used in building Deep Neural Networks. We then introduce Deep Neural Networks as well as topic modelling in Natural Language Processing (NLP), two advanced and currently very active fields in both industry and academia. Part III collects a range of case studies demonstrating how you can build a complete numerical application quickly from scratch using Owl. The cases presented include computer vision and recommender systems. This book aims at anyone with a basic knowledge of functional programming and a desire to explore the world of scientific computing, whether to generally explore the field in the round, to build applications for particular topics, or to deep-dive into how numerical systems are constructed. It does not assume strict ordering in reading – readers can simply jump to the topic that interests them most. HTTP:URL=https://doi.org/10.1007/978-3-030-97645-3
件　名	LCSH:Programming languages (Electronic computers) LCSH:Computer science -- Mathematics 全ての件名で検索 LCSH:Computers, Special purpose LCSH:Artificial intelligence -- Data processing 全ての件名で検索 FREE:Programming Language FREE:Mathematics of Computing FREE:Special Purpose and Application-Based Systems FREE:Data Science
分　類	LCC:QA76.7-.73 DC23:005.13
書誌ID	EB00003733
ISBN	9783030976453