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Books > Computing & IT > Applications of computing > Artificial intelligence > Knowledge-based systems / expert systems
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OCaml Scientific Computing - Functional Programming in Data Science and Artificial Intelligence (Paperback, 1st ed. 2022)
Loot Price: R1,586
Discovery Miles 15 860
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OCaml Scientific Computing - Functional Programming in Data Science and Artificial Intelligence (Paperback, 1st ed. 2022)
Series: Undergraduate Topics in Computer Science
Expected to ship within 10 - 15 working days
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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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