Articles liés à Math for Programming
Synopsis
A one-stop-shop for all the math you should have learned for your programming career.
Math for Programming summarizes all the core math topics a typical professional software engineer needs to know. The book condenses the various mathematics concepts covered in an undergraduate computer science program into a single volume, providing a starting point for independent study or a refresher for those who are some years removed from the classroom.
The book first covers preliminary subjects like number representation systems, set theory, and Boolean algebra. Then it dives into the field of discrete mathematics, including functions, induction proofs, number theory, combinatorics, graphs, and trees. The book also examines essential topics in probability, statistics, linear algebra, and calculus.
Rather than confine itself to abstract theory, the book focuses on practical application and numerical methods at the level typically encountered by working developers. Hands-on code examples in Python and C also make the topics concrete. Brush up on all the math you should have learned and level-up your career today.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Ronald T. Kneusel is a data scientist who builds deep-learning (AI) systems, as well as extensive experience with medical imaging and the development of medical devices. He earned a PhD in machine learning from the University of Colorado, Boulder, has nearly 20 years of machine learning experience in industry, and is presently pursuing deep-learning projects with L3Harris Technologies, Inc. Kneusel is also the author of Random Numbers and Computers (Springer 2018), in addition to Math for Deep Learning, Practical Deep Learning, and Strange Code—all published by No Starch Press.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Math for Programming
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Etat : New. Ronald T. Kneusel has been working with machine learning in industry since 2003 and has a PhD in machine learning from the University of Colorado, Boulder. Kneusel is the author of Practical Deep Learning, Math for Deep Learning, The. N° de réf. du vendeur 1149761480
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MATH FOR PROGRAMMING
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Math for Programming
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Paperback. Etat : New. This book summarizes all the core mathematical topics a typical professional software engineer needs to know. In condensing the various concepts covered in an undergraduate computer science program into a single volume, it provides an excellent starting point for independent study, or a refresher for those who haven't been in a classroom for years. Early chapters cover preliminary subjects like number representation systems, set theory, and Boolean algebra, followed by a dive into the field of discrete mathematics, including functions, induction proofs, number theory, combinatorics, graphs, and trees. Later sections examine essential topics in probability, statistics, linear algebra, and calculus. Rather than confine itself to abstract theory, the book focuses on practical applications and numerical methods at the level typically encountered by working software developers. In addition, hands-on code examples in Python and C make the topics concrete. N° de réf. du vendeur LU-9781718503588
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Math For Programming
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No Starch Press,US, 2025
ISBN 10 : 171850358X
ISBN 13 : 9781718503588
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Math for Programming : Learn the Math, Write the Better Code
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Math for Programming
Vendeur : Rarewaves.com UK, London, Royaume-Uni
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Paperback. Etat : New. This book summarizes all the core mathematical topics a typical professional software engineer needs to know. In condensing the various concepts covered in an undergraduate computer science program into a single volume, it provides an excellent starting point for independent study, or a refresher for those who haven't been in a classroom for years. Early chapters cover preliminary subjects like number representation systems, set theory, and Boolean algebra, followed by a dive into the field of discrete mathematics, including functions, induction proofs, number theory, combinatorics, graphs, and trees. Later sections examine essential topics in probability, statistics, linear algebra, and calculus. Rather than confine itself to abstract theory, the book focuses on practical applications and numerical methods at the level typically encountered by working software developers. In addition, hands-on code examples in Python and C make the topics concrete. N° de réf. du vendeur LU-9781718503588
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