Price: $55.00
(as of Nov 24,2024 00:28:25 UTC – Details)

Publisher ‏ : ‎ Symmetry Seed Books (April 19, 2024)
Language ‏ : ‎ English
Paperback ‏ : ‎ 385 pages
ISBN-10 ‏ : ‎ 0997116323
ISBN-13 ‏ : ‎ 978-0997116328
Item Weight ‏ : ‎ 1.44 pounds
Dimensions ‏ : ‎ 6 x 0.87 x 9 inches

Artificial intelligence (AI) has become an increasingly important and prevalent technology in our modern world. From self-driving cars to virtual assistants, AI is revolutionizing various industries and changing the way we interact with technology.

At the core of AI lies mathematics. The foundational mathematics of artificial intelligence encompasses a wide range of mathematical concepts and techniques that are essential for understanding and developing AI systems.

One of the key mathematical concepts used in AI is linear algebra. Linear algebra is the branch of mathematics that deals with vector spaces and linear mappings between these spaces. In the context of AI, linear algebra is used to represent and manipulate data in the form of vectors and matrices. This allows AI systems to perform operations like matrix multiplication, which are essential for tasks such as image recognition and natural language processing.

Another important mathematical concept in AI is calculus. Calculus is the branch of mathematics that deals with rates of change and accumulation of quantities. In AI, calculus is used to optimize and fine-tune algorithms through techniques like gradient descent. This allows AI systems to learn from data and improve their performance over time.

Probability and statistics are also fundamental to AI. Probability theory is used to model uncertainty and make predictions about future events, while statistics is used to analyze and interpret data. In AI, probabilistic models like Bayesian networks are used to make decisions under uncertainty, while statistical techniques like hypothesis testing are used to evaluate the performance of AI systems.

Overall, the foundational mathematics of artificial intelligence is crucial for understanding how AI systems work and for developing new and improved AI technologies. By mastering concepts like linear algebra, calculus, probability, and statistics, researchers and developers can unlock the full potential of AI and create innovative solutions to complex problems.
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