LEADER 05567nam 22007335 450 001 9910574070903321 005 20241111144340.0 010 $a3-030-97645-9 024 7 $a10.1007/978-3-030-97645-3 035 $a(MiAaPQ)EBC7001231 035 $a(Au-PeEL)EBL7001231 035 $a(CKB)22895136700041 035 $a(DE-He213)978-3-030-97645-3 035 $a(PPN)269148167 035 $a(EXLCZ)9922895136700041 100 $a20220526d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aOCaml Scientific Computing $eFunctional Programming in Data Science and Artificial Intelligence /$fby Liang Wang, Jianxin Zhao, Richard Mortier 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (372 pages) 225 1 $aUndergraduate Topics in Computer Science,$x2197-1781 311 08$aPrint version: Wang, Liang OCaml Scientific Computing Cham : Springer International Publishing AG,c2022 9783030976446 320 $aIncludes bibliographical references and index. 327 $aPart 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. 330 $aThis 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. . 410 0$aUndergraduate Topics in Computer Science,$x2197-1781 606 $aProgramming languages (Electronic computers) 606 $aComputer science$xMathematics 606 $aComputers, Special purpose 606 $aArtificial intelligence$xData processing 606 $aProgramming Language 606 $aMathematics of Computing 606 $aSpecial Purpose and Application-Based Systems 606 $aData Science 606 $aProgramaciķ orientada a l'objecte (Informātica)$2thub 606 $aProgramaciķ funcional (Informātica)$2thub 608 $aLlibres electrōnics$2thub 615 0$aProgramming languages (Electronic computers). 615 0$aComputer science$xMathematics. 615 0$aComputers, Special purpose. 615 0$aArtificial intelligence$xData processing. 615 14$aProgramming Language. 615 24$aMathematics of Computing. 615 24$aSpecial Purpose and Application-Based Systems. 615 24$aData Science. 615 7$aProgramaciķ orientada a l'objecte (Informātica) 615 7$aProgramaciķ funcional (Informātica) 676 $a005.114 676 $a005.133 700 $aWang$b Liang$f1975-$01270968 702 $aMortier$b Richard 702 $aZhao$b Jianxin$f1948- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910574070903321 996 $aOCaml scientific computing$92994034 997 $aUNINA