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Essential math for AI : = next-level mathematics for efficient and successful AI systems /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Essential math for AI :/ Hala Nelson.
Reminder of title:	next-level mathematics for efficient and successful AI systems /
remainder title:	Essential math for artificial intelligence :
Author:	Nelson, Hala,
Published:	Sebastopol, CA :O'Reilly Media, : 2023.,
Description:	xxxii, 568 p. :ill. ; : 24 cm.;
Notes:	Includes index.
Subject:	Artificial intelligence - Mathematics. -
ISBN:	9781098107635 (pbk.) :

Essential math for AI : = next-level mathematics for efficient and successful AI systems /
Nelson, Hala,

Essential math for AI :next-level mathematics for efficient and successful AI systems /Essential math for artificial intelligence :next-level mathematics for efficient and successful artificial intelligence systemsHala Nelson. - 1st ed. - Sebastopol, CA :O'Reilly Media,2023. - xxxii, 568 p. :ill. ;24 cm.

Includes index.

Why learn the mathematics of AI? -- Data, data, data -- Fitting functions to data -- Oprimization for neural networks -- Convolutional neural networks and computer vision -- Singular value decomposition : image processing, natural language processing, and social media -- Natural language and finance AI :vectorization and time series -- Probabilistic generative models -- Graph models -- Operations research -- Probability -- Mathematical logic -- Artificial intelligence and partial differential equations -- Artificial intelligence, ethics, mathematics, law and policy.

Many sectors and industries are eager to integrate AI and data-driven technologies into their systems and operations. But to build truly successful AI systems, you need a firm grasp of the underlying mathematics. This comprehensive guide bridges the current gap in presentation between the unlimited potential and applications of AI and its relevant mathematical foundations. Rather than discussing dense academic theory, author Hala Nelson surveys the mathematics necessary to thrive in the AI field, focusing on real-world applications and state-of-the-art models. You'll explore topics such as regression, neural networks, convolution, optimization, probability, Markov processes, differential equations, and more within an exclusive AI context. Engineers, data scientists, mathematicians, and scientists will gain a solid foundation for success in the AI and math fields.

ISBN: 9781098107635 (pbk.) :NT2601

LCCN: 2023278308

Nat. Bib. No.: GBC2K4877bnb

Nat. Bib. Agency Control No.: 020807688UkSubjects--Topical Terms:

840891
Artificial intelligence
--Mathematics.

LC Class. No.: Q335 / .N457 2023

Dewey Class. No.: 006.30151

Essential math for AI : = next-level mathematics for efficient and successful AI systems /
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300 $a xxxii, 568 p. : $b ill. ; $c 24 cm.
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505 0 # $a Why learn the mathematics of AI? -- Data, data, data -- Fitting functions to data -- Oprimization for neural networks -- Convolutional neural networks and computer vision -- Singular value decomposition : image processing, natural language processing, and social media -- Natural language and finance AI :vectorization and time series -- Probabilistic generative models -- Graph models -- Operations research -- Probability -- Mathematical logic -- Artificial intelligence and partial differential equations -- Artificial intelligence, ethics, mathematics, law and policy.
520 # $a Many sectors and industries are eager to integrate AI and data-driven technologies into their systems and operations. But to build truly successful AI systems, you need a firm grasp of the underlying mathematics. This comprehensive guide bridges the current gap in presentation between the unlimited potential and applications of AI and its relevant mathematical foundations. Rather than discussing dense academic theory, author Hala Nelson surveys the mathematics necessary to thrive in the AI field, focusing on real-world applications and state-of-the-art models. You'll explore topics such as regression, neural networks, convolution, optimization, probability, Markov processes, differential equations, and more within an exclusive AI context. Engineers, data scientists, mathematicians, and scientists will gain a solid foundation for success in the AI and math fields.
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