東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

OCaml scientific computing = functio...

Wang, Liang.

Linked to FindBook

Google Book

Amazon

博客來

OCaml scientific computing = functional programming in data science and artificial intelligence /

Record Type:	Electronic resources : Monograph/item
Title/Author:	OCaml scientific computing/ by Liang Wang, Jianxin Zhao, Richard Mortier.
Reminder of title:	functional programming in data science and artificial intelligence /
Author:	Wang, Liang.
other author:	Zhao, Jianxin.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	xxii, 359 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	OCaml (Computer program language) -
Online resource:	https://doi.org/10.1007/978-3-030-97645-3
ISBN:	9783030976453

OCaml scientific computing = functional programming in data science and artificial intelligence /
Wang, Liang.

OCaml scientific computingfunctional programming in data science and artificial intelligence /[electronic resource] :by Liang Wang, Jianxin Zhao, Richard Mortier. - Cham :Springer International Publishing :2022. - xxii, 359 p. :ill. (some col.), digital ;24 cm. - Undergraduate topics in computer science,2197-1781. - Undergraduate topics in computer science..

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.

ISBN: 9783030976453

Standard No.: 10.1007/978-3-030-97645-3doiSubjects--Topical Terms:

3597860
OCaml (Computer program language)

LC Class. No.: QA76.62

Dewey Class. No.: 005.114

OCaml scientific computing = functional programming in data science and artificial intelligence /
LDR:04000nmm a2200337 a 4500 001 2299932
003 DE-He213
005 20220526154656.0
006 m d
007 cr nn 008maaau
008 230324s2022 sz s 0 eng d
020 $a 9783030976453 $q (electronic bk.)
020 $a 9783030976446 $q (paper)
024 7 $a 10.1007/978-3-030-97645-3 $2 doi
035 $a 978-3-030-97645-3
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.62
072 7 $a UMX $2 bicssc
072 7 $a COM000000 $2 bisacsh
072 7 $a UMX $2 thema
082 0 4 $a 005.114 $2 23
090 $a QA76.62 $b .W246 2022
100 1 $a Wang, Liang. $3 1531217
245 1 0 $a OCaml scientific computing $h [electronic resource] : $b functional programming in data science and artificial intelligence / $c by Liang Wang, Jianxin Zhao, Richard Mortier.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a xxii, 359 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Undergraduate topics in computer science, $x 2197-1781
505 0 $a 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.
520 $a 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.
650 0 $a OCaml (Computer program language) $3 3597860
650 0 $a Functional programming (Computer science) $3 524481
650 1 4 $a Programming Language. $3 3538935
650 2 4 $a Mathematics of Computing. $3 891213
650 2 4 $a Special Purpose and Application-Based Systems. $3 892492
650 2 4 $a Data Science. $3 3538937
700 1 $a Zhao, Jianxin. $3 3430684
700 1 $a Mortier, Richard. $3 3597859
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Undergraduate topics in computer science. $3 1567579
856 4 0 $u https://doi.org/10.1007/978-3-030-97645-3
950 $a Computer Science (SpringerNature-11645)