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OCaml scientific computing = functio...

Wang, Liang.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	OCaml scientific computing/ by Liang Wang, Jianxin Zhao, Richard Mortier.
其他題名:	functional programming in data science and artificial intelligence /
作者:	Wang, Liang.
其他作者:	Zhao, Jianxin.
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	xxii, 359 p. :ill. (some col.), digital ;24 cm.
內容註:	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
標題:	OCaml (Computer program language) -
電子資源:	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

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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.
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