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

By: Publisher: Beijing ; Boston : O'Reilly, 2023Copyright date: ©2023Edition: First editionDescription: xxxii, 568 pages : illustrations ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781098107635
  • 1098107632
Other title:
  • Essential math for artificial intelligence : next-level mathematics for efficient and successful artificial intelligence systems
Subject(s): DDC classification:
  • 006.30151 23 N425
Contents:
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.
Summary: 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.
Item type: كتاب
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Item type Current library Call number Status Notes Date due Barcode
كتاب كتاب Central Library المكتبة المركزية 006.30151 N425 (Browse shelf(Opens below)) Available قاعة الكتب 48157

Includes bibliographical references and 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.