OCaml Scientific Computing: Functional Programming in Data Science and Artificial Intelligence

This book combines functional programming and numerical computation to develop data science applications. It introduces basic numerical techniques, advanced optimization techniques, and case studies in computer vision and recommender systems. Readers with functional programming knowledge can explore the field of scientific computing.

Format: Paperback / softback
Length: 359 pages
Publication date: 27 May 2022
Publisher: Springer Nature Switzerland AG

This book delves into the captivating realm of merging functional programming and numerical computation, unveiling the immense potential of OCaml in the development of data science applications. With a step-by-step approach, the authors guide readers through the intricate details of this harmonious synthesis, showcasing how the expressive nature of OCaml enables rapid and secure creation of cutting-edge data science solutions.

The journey begins by delving into the fundamental numerical techniques employed in OCaml, encompassing classical mathematical concepts such as interpolation, quadrature, statistics, and linear algebra. The authors then expand the scope, introducing multi-dimensional arrays, including the powerful tensor and Ndarray, as the cornerstone data types in any numerical computing system. This comprehensive exploration culminates in two additional classical numerical computing topics: the solution of Ordinary Differential Equations (ODEs) and Signal Processing, while also introducing the visualization module that serves as a thread throughout the book.

Part II of this remarkable work takes readers on an advanced optimization journey, focusing not only on practical applications but also on the fundamental building blocks that underpin these fields. The spotlight shines brightly on Algorithmic Differentiation, considered the pivotal component that empowers the construction of Deep Neural Networks. Subsequent chapters delve into Optimization and Regression, essential tools utilized in the development of Deep Neural Networks. Furthermore, the authors introduce Deep Neural Networks and Topic Modeling in Natural Language Processing (NLP), two rapidly evolving and highly influential domains in both industry and academia.

In Part III, the authors present a collection of captivating case studies, showcasing real-world applications and demonstrating how to build robust and scalable data science systems. These case studies cover a diverse range of domains, including machine learning, computer vision, and natural language processing, providing valuable insights and practical methodologies for developing data science solutions that can meet the demands of modern computing environments.

By combining functional programming principles with numerical computation expertise, this book offers a comprehensive guide for data scientists, software engineers, and researchers seeking to harness the power of OCaml in their endeavors. It serves as a valuable resource for anyone looking to advance their knowledge and skills in the field of data science, enabling them to create innovative and effective solutions that drive progress and innovation.

Weight: 587g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030976446
Edition number: 1st ed. 2022