Application of Linear Algebra in Enhancing Artificial Intelligence Techniques
DOI:
https://doi.org/10.65405/.v10i37.624Abstract
his paper examines the fundamental relationship between linear algebra and artificial
intelligence techniques, as linear algebra represents the fundamental framework upon which many
machine learning algorithms are based.
By analyzing mathematical concepts such as matrices, vectors, eigenvalues, and singular value
decomposition (SVD), we highlight how these concepts can be harnessed to improve the
performance of intelligent models and develop effective techniques for data analysis, classification,
and prediction. We also present applied experiments that demonstrate the practical use of linear
algebra in modern artificial intelligence models
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References
[1] Strang, G. (2016). Introduction to Linear Algebra. Wellesley-Cambridge Press.
[2] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
[3] Shlens, J. (2014). A Tutorial on Principal Component Analysis. arXiv:1404.1100.
[4] Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.
[5] Lay, D. C. (2012). Linear Algebra and Its Applications. Pearson Education.
[6] Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical
Learning. Springer.
[7] Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
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