000			04777nam a22004335i 4500
988			_aSpringer_Computer_2022
999			_c382955 _d382955 _x1
001			382955
003			ES-MaUEC
005			20230102122029.0
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007			cr nn 008mamaa
008			221008s2022 sz | s |||| 0|eng d
020			_a9783030976453
024	7		_a10.1007/978-3-030-97645-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.62 _b2022 EB
100	1		_aWang, Liang _eauthor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9684958
245	1	0	_aOCaml Scientific Computing : _bFunctional Programming in Data Science and Artificial Intelligence _cby Liang Wang, Jianxin Zhao, Richard Mortier
250			_aFirst edition 2022
264		1	_aCham _bSpringer International Publising _c2022
300			_a1 recurso en línea (XXII, 359 páginas) _b105 ilustraciones, 73 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aUndergraduate Topics in Computer Science _x2197-1781
505	0		_aPart 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.
520			_aThis 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. .
650		7	_2embne _9158251 _aProgramación funcional (Informática)
700	1		_aZhao, Jianxin _eauthor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9684959
700	1		_aMortier, Richard _eauthor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9684960
776	0	8	_iPrinted edition: _z9783030976446
776	0	8	_iPrinted edition: _z9783030976460
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-97645-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b10/2022 _dz _eIG _zSI