OCaml Scientific Computing: Functional Programming in Data Science and Artificial Intelligence 1st ed. 2022 Edition

“This book should be read as a tutorial on the facilities of the Owl library. ... OCaml scientific computing serves as a valuable resource for computer science students embarking on their initial exploration of numerical algorithms. Whether you are diving into numerical methods for the first time or you are experienced with numerical algorithms and want to understand how to use OCaml for this purpose, this book provides practical guidance and bridges theory with hands-on implementation.” (Prahladavaradan Sampath, Computing Reviews, September 18, 2024)

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 offunctional 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.