Essential Math for AI: Next-Level Mathematics for Efficient and Successful AI Systems 1st Edition 274063
Код товару: 274063Паперова книга
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ISBN978-1098107635
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Бренд
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Автор
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Рік2023
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МоваАнглійська
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ІлюстраціїЧорно-білі
All the math we need to get into AI. Math and AI made easy...
Many 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 gap in presentation between the potential and applications of AI and its relevant mathematical foundations.
In an immersive and conversational style, the book surveys the mathematics necessary to thrive in the AI field, focusing on real-world applications and state-of-the-art models, rather than on dense academic theory. You'll explore topics such as regression, neural networks, convolution, optimization, probability, graphs, random walks, Markov processes, differential equations, and more within an exclusive AI context geared toward computer vision, natural language processing, generative models, reinforcement learning, operations research, and automated systems. With a broad audience in mind, including engineers, data scientists, mathematicians, scientists, and people early in their careers, the book helps build a solid foundation for success in the AI and math fields.
You'll be able to:
Comfortably speak the languages of AI, machine learning, data science, and mathematics
- Unify machine learning models and natural language models under one mathematical structure
- Handle graph and network data with ease
- Explore real data, visualize space transformations, reduce dimensions, and process images
- Decide on which models to use for different data-driven projects
- Explore the various implications and limitations of AI
About the Author
Hala Nelson is an Associate Professor of Mathematics at James Madison University. She has a Ph.D. in mathematics from the Courant Institute of Mathematical Sciences at New York University. Prior to James Madison University, she was a postdoctoral assistant professor at the University of Michigan, Ann Arbor.
She specializes in mathematical modeling and consults for emergency and infrastructure services in the public sector. She likes to translate complex ideas into simple and practical terms. To her, most mathematical concepts are painless and relatable, unless the person presenting them either does not understand them very well or is trying to show off.
Other facts: Hala Nelson grew up in Lebanon during its brutal civil war. She lost her hair at a very young age in a missile explosion. This event, and many that followed, shaped her interests in human behavior, the nature of intelligence, and AI. Her dad taught her math, at home and in French, until she graduated high school. Her favorite quote from her dad about math is, "It is the one clean science".
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Характеристики
- Бренд
- Автор
- КатегоріяПрограмування
- Рік2023
- Сторінок602
- Формат165х235 мм
- ОбкладинкаМ'яка
- Тип паперуОфсетний
- МоваАнглійська
- ІлюстраціїЧорно-білі
Від видавця
All the math we need to get into AI. Math and AI made easy...
Many 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 gap in presentation between the potential and applications of AI and its relevant mathematical foundations.
In an immersive and conversational style, the book surveys the mathematics necessary to thrive in the AI field, focusing on real-world applications and state-of-the-art models, rather than on dense academic theory. You'll explore topics such as regression, neural networks, convolution, optimization, probability, graphs, random walks, Markov processes, differential equations, and more within an exclusive AI context geared toward computer vision, natural language processing, generative models, reinforcement learning, operations research, and automated systems. With a broad audience in mind, including engineers, data scientists, mathematicians, scientists, and people early in their careers, the book helps build a solid foundation for success in the AI and math fields.
You'll be able to:
Comfortably speak the languages of AI, machine learning, data science, and mathematics
- Unify machine learning models and natural language models under one mathematical structure
- Handle graph and network data with ease
- Explore real data, visualize space transformations, reduce dimensions, and process images
- Decide on which models to use for different data-driven projects
- Explore the various implications and limitations of AI
About the Author
Hala Nelson is an Associate Professor of Mathematics at James Madison University. She has a Ph.D. in mathematics from the Courant Institute of Mathematical Sciences at New York University. Prior to James Madison University, she was a postdoctoral assistant professor at the University of Michigan, Ann Arbor.
She specializes in mathematical modeling and consults for emergency and infrastructure services in the public sector. She likes to translate complex ideas into simple and practical terms. To her, most mathematical concepts are painless and relatable, unless the person presenting them either does not understand them very well or is trying to show off.
Other facts: Hala Nelson grew up in Lebanon during its brutal civil war. She lost her hair at a very young age in a missile explosion. This event, and many that followed, shaped her interests in human behavior, the nature of intelligence, and AI. Her dad taught her math, at home and in French, until she graduated high school. Her favorite quote from her dad about math is, "It is the one clean science".
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