OCaml scientific computing : functional programming in data science and artificial intelligence / Liang Wang, Jianxin Zhao, Richard Mortier.
Wang, Liang, author.; Zhao, Jianxin, author.; Mortier, Richard, author.
2022
QA76.62
Available Online

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Title OCaml scientific computing : functional programming in data science and artificial intelligence / Liang Wang, Jianxin Zhao, Richard Mortier.
Author Wang, Liang, author.
ISBN 9783030976453 (electronic bk.)
3030976459 (electronic bk.)
9783030976446 (print)
3030976440
DOI https://doi.org/10.1007/978-3-030-97645-3
Published Cham, Switzerland : Springer, 2022.
Language English
Description 1 online resource (xxii, 359 pages) : illustrations (some color).
Other Standard Identifiers 10.1007/978-3-030-97645-3 doi
Call Number QA76.62
Dewey Decimal Classification 005.1/14
Summary 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. .
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed June 2, 2022).
Added Author Zhao, Jianxin, author.
Mortier, Richard, author.
Series Undergraduate topics in computer science, 2197-1781
Available in Other Form Print version: 9783030976446
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
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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.

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